system

Abstract

The system according to this embodiment aims to analyze endoscopic images and other medical images and propose personalized diagnoses and treatment plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a classification unit, a prediction unit, and a proposal unit. The collection unit collects endoscopic images and other medical images, as well as past examination data and medical records of patients. The analysis unit analyzes the data collected by the collection unit to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. The prediction unit predicts the malignancy of the lesions classified by the classification unit. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the diagnostic accuracy is not sufficient in the analysis of endoscopic images and other medical images, and it is difficult to propose an individualized diagnosis and treatment plan.

[0005] The system according to the embodiment aims to analyze endoscopic images and other medical images and propose an individualized diagnosis and treatment plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a classification unit, a prediction unit, and a proposal unit. The data collection unit collects endoscopic images, other medical images, and past examination data and medical records of patients. The analysis unit analyzes the data collected by the data collection unit to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. The prediction unit predicts the malignancy of the lesions classified by the classification unit. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze endoscopic images and other medical images and propose personalized diagnoses and treatment plans. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The diagnostic support system according to an embodiment of the present invention is a system that uses AI to improve diagnostic accuracy in the medical field. This diagnostic support system collects endoscopic images, other medical images, past examination data and medical records of patients, and the AI ​​analyzes them to detect and classify lesions and predict their malignancy. Furthermore, it automatically classifies lesions and predicts their malignancy based on the analysis results. This supports physicians' diagnoses and prevents oversights. In addition, the AI ​​integrates past examination data, medical records, genetic information, etc., and proposes a diagnosis and treatment plan optimized for each individual patient. The system continuously learns and incorporates new cases and the latest research results to constantly improve diagnostic accuracy. Furthermore, it automatically summarizes examination results and generates detailed reports, significantly improving the efficiency of physicians' work. For example, the diagnostic support system collects images in real time during endoscopic examinations, and the AI ​​immediately performs analysis. The analysis results indicate the presence or absence of lesions, their type, and malignancy, and are quickly fed back to the physician. This allows physicians to make immediate diagnoses during examinations. The diagnostic support system also integrates past examination data and medical records of patients, and the AI ​​performs analysis to propose individualized diagnoses and treatment plans. For example, the system considers the patient's genetic information and lifestyle data to suggest the optimal treatment options. This ensures that the most appropriate treatment is provided for each individual patient. Furthermore, the diagnostic support system continuously learns and incorporates new cases and the latest research findings to constantly improve diagnostic accuracy. For instance, if a new lesion pattern or treatment method is discovered, the system learns about it and incorporates it into the next diagnosis. This allows the diagnostic support system to always make diagnoses based on the latest medical knowledge. In this way, the diagnostic support system can support physicians' diagnoses and prevent oversights.

[0029] The diagnostic support system according to this embodiment comprises a data collection unit, an analysis unit, a classification unit, a prediction unit, and a proposal unit. The data collection unit collects endoscopic images, other medical images, and past examination data and medical records of patients. For example, the data collection unit collects images acquired during an endoscopic examination in real time and immediately saves them to a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and save them to an integrated database. Furthermore, the data collection unit can automatically classify the collected image data and save it to an appropriate folder. For example, the data collection unit collects images acquired during an endoscopic examination in real time and immediately saves them to a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and save them to an integrated database. The data collection unit can also automatically classify the collected image data and save it to an appropriate folder. The analysis unit analyzes the data collected by the data collection unit to detect and classify lesions and predict their malignancy. For example, the analysis unit analyzes endoscopic images in real time to detect lesions. The analysis unit can also analyze past examination data of patients to evaluate the progression of lesions. Furthermore, the analysis unit can also analyze the patient's medical records and identify risk factors for lesions. For example, the analysis unit can analyze images acquired during endoscopic examinations in real time to detect lesions. The analysis unit can also analyze the patient's past examination data to assess the progression of lesions. The analysis unit can also analyze the patient's medical records and identify risk factors for lesions. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. For example, the classification unit analyzes endoscopic images and classifies lesions into benign and malignant. The classification unit can also analyze the patient's past examination data and classify lesions based on their progression. Furthermore, the classification unit can analyze the patient's medical records and classify lesions based on risk factors. For example, the classification unit analyzes endoscopic images and classifies lesions into benign and malignant. The classification unit can also analyze the patient's past examination data and classify lesions based on their progression. The classification unit can also analyze the patient's medical records and classify lesions based on risk factors. The prediction unit predicts the malignancy of lesions classified by the classification unit.The prediction unit, for example, analyzes endoscopic images and predicts the degree of malignancy based on the shape and size of the lesion. The prediction unit can also analyze the patient's past examination data and predict the risk of lesion progression. Furthermore, the prediction unit can analyze the patient's medical records and predict the risk of lesion recurrence. For example, the prediction unit analyzes endoscopic images and predicts the degree of malignancy based on the shape and size of the lesion. The prediction unit can also analyze the patient's past examination data and predict the risk of lesion progression. The prediction unit can also analyze the patient's medical records and predict the risk of lesion recurrence. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. For example, the proposal unit integrates the patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. Furthermore, the proposal unit can also propose an individualized treatment plan considering the patient's lifestyle data. Furthermore, the proposal unit can also propose an individualized diagnosis and treatment plan considering the patient's current health status. For example, the proposal unit integrates the patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. The proposal unit can also propose an individualized treatment plan, taking into account the patient's lifestyle data. The proposal unit can also propose an individualized diagnosis and treatment plan, taking into account the patient's current health status. This allows the diagnostic support system according to the embodiment to support the physician's diagnosis and prevent oversights. Some or all of the above-described processes in the collection unit, analysis unit, classification unit, prediction unit, and proposal unit may be performed using AI, for example, or not using AI. For example, the collection unit collects images acquired during endoscopic examination in real time and immediately stores them in a database. The analysis unit analyzes the data collected by the collection unit to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. The prediction unit predicts the malignancy of the lesions classified by the classification unit. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit.

[0030] The data collection unit collects endoscopic images, other medical images, and past examination data and medical records of patients. Specifically, it collects images acquired during endoscopic examinations in real time and immediately saves them to a database. The endoscopic images are high resolution and designed to ensure that even minute lesions are not missed. The data collection unit can also simultaneously collect other medical images such as CT scans and MRIs and store this data in an integrated database. This allows for centralized management of different types of medical images, making them easily accessible to the analysis and classification units. Furthermore, the data collection unit has the function of automatically classifying the collected image data and saving it to the appropriate folders. For example, endoscopic images are classified into categories such as the digestive system and respiratory system, and CT scans and MRI images are classified into categories such as the brain, chest, and abdomen. This allows for quick retrieval of necessary data and its use in analysis. The data collection unit also collects past examination data and medical records of patients and stores this data in an integrated database. Past examination data includes blood test results, pathology test results, and genetic information, and this data is useful for identifying the progression of lesions and risk factors. The data collection unit automatically updates this data, ensuring that the latest information is always maintained. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, for acutely ill patients, the data collection frequency can be increased, and if a rapid response is required, data can be collected in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to detect and classify lesions and predict their malignancy. Specifically, it analyzes endoscopic images in real time to detect lesions. By utilizing AI-based image recognition technology, it can detect minute lesions and abnormalities with high accuracy. For example, it can automatically identify lesions such as polyps and ulcers in endoscopic images and determine their location and size. The analysis unit can also analyze the patient's past examination data to evaluate the progression of lesions. Past examination data includes blood test results and pathology test results, and by statistically analyzing this data, it identifies the rate of lesion progression and risk factors. Furthermore, the analysis unit can analyze the patient's medical records to identify risk factors for lesions. For example, it can analyze the patient's lifestyle and genetic information to evaluate how specific risk factors affect the occurrence and progression of lesions. The analysis unit can integrate and analyze this data to perform a comprehensive risk assessment. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past endoscopic image data, it can predict the incidence and progression of lesions in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The classification unit automatically classifies lesions based on the results obtained by the analysis unit. Specifically, it analyzes endoscopic images and classifies lesions into benign and malignant categories. Utilizing AI-based classification algorithms, it performs highly accurate classification based on characteristics such as shape, color, and texture of the lesions. For example, it analyzes differences in polyp shape and color to automatically identify benign and malignant polyps. The classification unit can also analyze a patient's past examination data and classify lesions based on their progression. This data includes pathology and blood test results. Based on this data, it evaluates the progression and risk of lesions and classifies them into appropriate categories. Furthermore, the classification unit can analyze a patient's medical records and classify lesions based on risk factors. For example, it analyzes a patient's lifestyle and genetic information to evaluate how specific risk factors affect the development and progression of lesions and identify high-risk patients. The classification unit can comprehensively analyze this data to perform a comprehensive risk assessment. Additionally, the classification unit can continuously revise its classification results based on real-time updated data to adapt to the latest situations. For example, if new test data or medical records are added, the classification unit immediately incorporates the new data and updates the classification results. Furthermore, the classification unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the classification unit to not only monitor the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0033] The prediction unit predicts the malignancy of lesions classified by the classification unit. Specifically, it analyzes endoscopic images and predicts malignancy based on the shape and size of the lesion. By utilizing an AI-based prediction algorithm and analyzing the characteristics of the lesion in detail, it performs highly accurate malignancy predictions. For example, it analyzes the shape, size, and color changes of polyps to identify polyps with a high degree of malignancy. The prediction unit can also analyze the patient's past examination data to predict the risk of lesion progression. Past examination data includes pathology test results and blood test results, and based on this data, it evaluates the rate and risk of lesion progression and predicts future risk. Furthermore, the prediction unit can analyze the patient's medical records to predict the risk of lesion recurrence. For example, it analyzes the patient's lifestyle and genetic information to evaluate how specific risk factors affect lesion recurrence and identify patients at high risk of recurrence. The prediction unit can integrate and analyze this data to perform a comprehensive risk assessment. In addition, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if new test data or medical records are added, the prediction unit immediately incorporates the new data and updates the prediction results. Furthermore, the prediction unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the prediction unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0034] The proposal unit proposes personalized diagnoses and treatment plans based on the prediction results obtained by the prediction unit. Specifically, it integrates the patient's past test data, medical records, and genetic information to propose a diagnosis and treatment plan optimized for each individual patient. Utilizing AI-powered diagnostic support algorithms, it develops optimal treatment plans by analyzing the patient's characteristics and risk factors in detail. For example, based on the patient's past test data, it evaluates the effectiveness of a particular treatment and proposes the optimal treatment. The proposal unit can also propose personalized treatment plans considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits, evaluates the impact of lifestyle improvements on treatment, and proposes specific improvement measures. Furthermore, the proposal unit can propose personalized diagnoses and treatment plans considering the patient's current health status. For example, based on the patient's latest test results and medical records, it proposes the most suitable treatment for their current health condition. The proposal unit can comprehensively analyze this data to develop a comprehensive diagnosis and treatment plan. In addition, the proposal unit can continuously modify its proposals based on real-time updated data to adapt to the latest situation. For example, if new test data or medical records are added, the proposal unit immediately incorporates the new data and updates its proposals. Furthermore, the proposed system can use an anomaly detection algorithm to detect unusual patterns and abnormal data, and issue warnings early. This allows the system to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0035] The learning unit can continuously train the generative AI. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. Furthermore, the learning unit can adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. The learning unit can also adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process. This allows the generative AI to continuously learn, improving the accuracy of the system. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. The learning unit can also adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process.

[0036] The report generation unit can automatically summarize test results and generate a detailed report. For example, the report generation unit can automatically summarize test results using a summarization algorithm. It can also perform summarization based on the length of the summary and the importance of the information being summarized. Furthermore, the report generation unit can adjust the items, format, and depth of information in the detailed report. This improves the efficiency of physicians by automatically summarizing test results and generating a detailed report. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can automatically summarize test results using a summarization algorithm. It can also perform summarization based on the length of the summary and the importance of the information being summarized. The report generation unit can also adjust the items, format, and depth of information in the detailed report.

[0037] The data collection unit can collect endoscopic images and other medical images in real time and store them in a database. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in the database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. Furthermore, the data collection unit can automatically classify the collected image data and save it to the appropriate folder. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. The data collection unit can also automatically classify the collected image data and save it in the appropriate folder. This enables rapid data analysis by collecting endoscopic images and other medical images in real time and storing them in a database. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. The collection unit can also automatically classify the collected image data and save it to the appropriate folder.

[0038] The data collection unit can automatically retrieve past examination data from the electronic medical record system. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the electronic medical record system. Furthermore, the data collection unit can retrieve past blood test results and biometric information from the electronic medical record system and store them in an integrated database. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the electronic medical record system. The data collection unit can also retrieve past blood test results and biometric information from the electronic medical record system and store them in an integrated database. This makes data integration easier by automatically retrieving past examination data from the electronic medical record system. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the patient's electronic medical record system. Furthermore, it can retrieve past blood test results and biometric information from the patient's electronic medical record system and store them in an integrated database.

[0039] The data collection unit can collect and integrate a patient's medical records from multiple medical institutions. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. Furthermore, the data collection unit can integrate a patient's past medical records through an electronic medical record system and store them in a database. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. The data collection unit can also integrate a patient's past medical records through an electronic medical record system and store them in a database. This enables a comprehensive diagnosis by collecting a patient's medical records from multiple medical institutions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. The data collection unit can also integrate patients' past medical records through the electronic medical record system and store them in a database.

[0040] The data collection unit can collect patients' lifestyle data and integrate and analyze it with medical data. For example, the data collection unit can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The data collection unit can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. Furthermore, the data collection unit can collect patients' smoking and drinking habits and integrate and analyze them with medical data. This allows for a more comprehensive diagnosis by collecting patients' lifestyle data and integrating and analyzing it with medical data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The data collection unit can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. The data collection unit can also collect patients' smoking and drinking habits and integrate and analyze them with medical data.

[0041] The data collection unit can filter data based on the patient's current health status and past treatment history. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. Furthermore, the data collection unit can filter and collect specific data based on the patient's current symptoms. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. The data collection unit can also filter and collect specific data based on the patient's current symptoms. This allows for the efficient collection of only the necessary data by filtering data based on the patient's current health status and past treatment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. The data collection unit can also filter and collect specific data based on the patient's current symptoms.

[0042] The analysis unit can analyze endoscopic images in real time and detect lesions. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. Furthermore, the analysis unit can save the analysis results of the endoscopic images to a database for later reference. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. The analysis unit can also save the analysis results of the endoscopic images to a database for later reference. This enables rapid diagnosis by analyzing endoscopic images in real time and detecting lesions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. The analysis unit can also save the analysis results of the endoscopic images to a database for later reference.

[0043] The analysis unit can analyze a patient's past examination data and evaluate the progression of the disease. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. Furthermore, the analysis unit can analyze a patient's past blood test results and evaluate the progression of the disease. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past blood test results and evaluate the progression of the disease. This allows for the development of an appropriate treatment plan by analyzing a patient's past examination data and evaluating the progression of the disease. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. The analysis unit can also analyze the patient's past blood test results to assess the progression of the disease.

[0044] The analysis unit can analyze a patient's medical records and identify risk factors for lesions. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. Furthermore, the analysis unit can analyze a patient's lifestyle data and identify risk factors for lesions. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. The analysis unit can also analyze a patient's lifestyle data and identify risk factors for lesions. This allows for preventative treatment by analyzing a patient's medical records and identifying risk factors for lesions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. The analysis unit can also analyze a patient's lifestyle data and identify risk factors for lesions.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data.

[0046] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a specific analysis algorithm to endoscopic images. The analysis unit can also apply different analysis algorithms to CT scan and MRI data. Furthermore, the analysis unit can apply specific analysis algorithms to blood test results. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply a specific analysis algorithm to endoscopic images. The analysis unit can also apply different analysis algorithms to CT scan and MRI data. The analysis unit can also apply specific analysis algorithms to blood test results.

[0047] The classification unit can analyze endoscopic images and automatically classify lesions. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). Furthermore, the classification unit can analyze endoscopic images and classify lesions based on their size and shape. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). This enables rapid diagnosis by analyzing endoscopic images and automatically classifying lesions. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). The classification unit can also analyze endoscopic images and classify lesions based on their size and shape.

[0048] The classification unit can analyze a patient's past examination data and classify them based on the progression of the disease. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past CT scan and MRI data and classify them based on the progression of the disease. Furthermore, the classification unit can analyze a patient's past blood test results and classify them based on the progression of the disease. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past CT scan and MRI data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past blood test results and classify them based on the progression of the disease. This allows for the creation of an appropriate treatment plan by analyzing a patient's past examination data and classifying them based on the progression of the disease. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze past CT scan and MRI data of patients and classify them based on the progression of their lesions. The classification unit can also analyze past blood test results of patients and classify them based on the progression of their lesions.

[0049] The classification unit can analyze a patient's medical records and classify them based on the risk factors for lesions. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. Furthermore, the classification unit can analyze a patient's lifestyle data and classify it based on the risk factors for lesions. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. The classification unit can also analyze a patient's lifestyle data and classify it based on the risk factors for lesions. This makes preventive treatment possible by analyzing a patient's medical records and classifying them based on the risk factors for lesions. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. The classification unit can also analyze a patient's lifestyle data and classify it based on the risk factors for lesions.

[0050] The classification unit can improve the accuracy of classification by considering the interrelationships between data. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. Furthermore, the classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's medical records. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's medical records. This allows for more accurate diagnoses by improving the accuracy of classification by considering the interrelationships between data. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. The classification unit can also integrate endoscopic images and patient medical records to improve the accuracy of classification.

[0051] The classification unit can perform classification by considering the attribute information of the data submitter. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. Furthermore, the classification unit can classify data based on the patient's age and gender. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. The classification unit can also classify data based on the patient's age and gender. This allows for more appropriate classification by considering the attribute information of the data submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. The classification unit can also classify data based on the patient's age and gender.

[0052] The prediction unit can analyze endoscopic images and predict the malignancy of a lesion. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. Furthermore, the prediction unit can analyze endoscopic images and predict the malignancy based on the location of the lesion and its relationship to surrounding tissue. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. It can also analyze endoscopic images and predict the malignancy based on the location of the lesion and its relationship to surrounding tissue. This enables rapid diagnosis by analyzing endoscopic images and predicting the malignancy of the lesion. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. The prediction unit can also analyze endoscopic images and predict the degree of malignancy based on the location of the lesion and its relationship to surrounding tissue.

[0053] The prediction unit can analyze a patient's past examination data and predict the risk of disease progression. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. Furthermore, the prediction unit can analyze a patient's past blood test results to predict the risk of disease progression. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. The prediction unit can also analyze a patient's past blood test results to predict the risk of disease progression. This allows for the development of an appropriate treatment plan by analyzing a patient's past examination data and predicting the risk of disease progression. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. The prediction unit can also analyze the patient's past blood test results to predict the risk of disease progression.

[0054] The prediction unit can analyze a patient's medical records and predict the risk of lesion recurrence. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. Furthermore, the prediction unit can analyze a patient's lifestyle data and predict the risk of lesion recurrence. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's lifestyle data and predict the risk of lesion recurrence. This makes preventive treatment possible by analyzing a patient's medical records and predicting the risk of lesion recurrence. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's lifestyle data and predict the risk of lesion recurrence.

[0055] The prediction unit can optimize the prediction algorithm by referring to past prediction data. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. Furthermore, the prediction unit can learn the prediction algorithm by referring to past prediction data. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. The prediction unit can also learn the prediction algorithm by referring to past prediction data. This improves prediction accuracy by optimizing the prediction algorithm by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. The prediction unit can also learn the prediction algorithm by referring to past prediction data.

[0056] The prediction unit can apply different prediction methods to each data category. For example, the prediction unit can apply a specific prediction method to endoscopic images. It can also apply different prediction methods to CT scan and MRI data. Furthermore, the prediction unit can apply specific prediction methods to blood test results. This allows for more accurate predictions by applying different prediction methods to each data category. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can apply a specific prediction method to endoscopic images. It can also apply different prediction methods to CT scan and MRI data. It can also apply specific prediction methods to blood test results.

[0057] The suggestion unit can integrate a patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. It can also propose an individualized diagnosis based on a patient's genetic information. Furthermore, the suggestion unit can integrate a patient's medical records to propose an optimal treatment plan. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. The suggestion unit can also propose an individualized diagnosis based on a patient's genetic information. The suggestion unit can also integrate a patient's medical records to propose an optimal treatment plan. This allows for the proposal of an individualized diagnosis and treatment plan by integrating a patient's past examination data, medical records, genetic information, etc. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. The suggestion unit can also propose an individualized diagnosis based on a patient's genetic information. The suggestion unit can also integrate a patient's medical records to propose an optimal treatment plan.

[0058] The suggestion unit can propose an individualized treatment plan by considering the patient's lifestyle data. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. Furthermore, the suggestion unit can propose an optimal treatment plan based on the patient's smoking and drinking habits. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. The suggestion unit can also propose an optimal treatment plan based on the patient's smoking and drinking habits. This allows for the proposal of a more appropriate treatment plan by considering the patient's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. The suggestion unit can also propose an optimal treatment plan based on the patient's smoking and drinking habits.

[0059] The suggestion unit can propose an individualized diagnosis and treatment plan, taking into account the patient's current health condition. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. Furthermore, the suggestion unit can propose an optimal treatment plan based on the patient's current treatment history. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. The suggestion unit can also propose an optimal treatment plan, taking into account the patient's current treatment history. This allows for the proposal of a more appropriate diagnosis and treatment plan by considering the patient's current health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. The suggestion unit can also propose an optimal treatment plan, taking into account the patient's current treatment history.

[0060] The suggestion unit can adjust the level of detail of its suggestions based on the patient's importance. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. Furthermore, the suggestion unit can prioritize suggestions according to the patient's importance. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. The suggestion unit can also prioritize suggestions according to the patient's importance. This allows for efficient suggestions by adjusting the level of detail of suggestions based on the patient's importance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. The suggestion unit can also prioritize suggestions according to the patient's importance.

[0061] The suggestion unit can apply different suggestion algorithms depending on the patient category. For example, the suggestion unit can apply a specific suggestion algorithm to patients who have undergone an endoscopy. Furthermore, the suggestion unit can apply a different suggestion algorithm to patients who have CT scan or MRI data. Additionally, the suggestion unit can apply a specific suggestion algorithm to patients who have blood test results. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the patient category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can apply a specific suggestion algorithm to patients who have undergone an endoscopy. The suggestion unit can also apply a different suggestion algorithm to patients who have CT scan or MRI data. The suggestion unit can also apply a specific suggestion algorithm to patients who have blood test results.

[0062] The learning unit can optimize the learning algorithm by referring to past training data. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can train the learning algorithm by referring to past training data. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. The learning unit can also train the learning algorithm by referring to past training data. This improves learning accuracy by optimizing the learning algorithm by referring to past training data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. The learning unit can also train the learning algorithm by referring to past training data.

[0063] The learning unit can weight the training data based on when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. Furthermore, the learning unit can adjust the weighting of the training data according to when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. The learning unit can also adjust the weighting of the training data according to when the data was collected. This allows for more effective learning by weighting the training data based on when the data was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. The learning unit can also adjust the weighting of the training data according to when the data was collected.

[0064] The report generation unit can adjust the level of detail in the report based on the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. Furthermore, the report generation unit can determine the priority of reports according to the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. It can also determine the priority of reports according to the importance of the data. This allows for efficient report generation by adjusting the level of detail in the report based on the importance of the data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. It can also determine the priority of reports according to the importance of the data.

[0065] The report generation unit can determine the priority of reports based on the data collection timing. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. Furthermore, the report generation unit can determine the priority of reports according to the data collection timing. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. The report generation unit can also determine the priority of reports according to the data collection timing. This enables efficient report generation by determining the priority of reports based on the data collection timing. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without using AI. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. The report generation unit can also determine the priority of reports according to the data collection timing.

[0066] The report generation unit can adjust the level of detail in a report based on the importance of the data during report generation. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. Furthermore, the report generation unit can determine the priority of reports according to the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. The report generation unit can also determine the priority of reports according to the importance of the data. This allows for efficient report generation by adjusting the level of detail in reports based on the importance of the data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. The report generation unit can also determine the priority of reports according to the importance of the data.

[0067] The report generation unit can adjust the report content when generating a report, taking into account the user's current health status. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. Furthermore, the report generation unit can optimize the report content based on the user's current treatment history. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. The report generation unit can also optimize the report content based on the user's current treatment history. This allows for the provision of more appropriate reports by adjusting the report content to take into account the user's current health status. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. The report generation unit can also optimize the report content based on the user's current treatment history.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] A diagnostic support system can collect patients' lifestyle data and integrate and analyze it with medical data. For example, it can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. It can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. Furthermore, it can collect patients' smoking and drinking habits and integrate and analyze them with medical data. This allows for a more comprehensive diagnosis by collecting patients' lifestyle data and integrating and analyzing it with medical data. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or not. For example, the diagnostic support system can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The diagnostic support system can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. The diagnostic support system can also collect patients' smoking and drinking habits and integrate and analyze them with medical data.

[0070] The diagnostic support system can automatically retrieve a patient's past examination data from the electronic medical record system. For example, it can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. It can also collect the patient's past CT scan and MRI data from the electronic medical record system. Furthermore, it can retrieve the patient's past blood test results and biometric information from the electronic medical record system and store them in an integrated database. This makes data integration easier by automatically retrieving the patient's past examination data from the electronic medical record system. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or without AI. For example, the diagnostic support system can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The diagnostic support system can also collect the patient's past CT scan and MRI data from the electronic medical record system. The diagnostic support system can also retrieve the patient's past blood test results and biometric information from the electronic medical record system and store them in an integrated database.

[0071] A diagnostic support system can collect and integrate a patient's medical records from multiple medical institutions. For example, it can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. It can also collect the patient's past surgical records and treatment history from multiple medical institutions. Furthermore, it can integrate the patient's past medical records through an electronic medical record system and store them in a database. This enables a comprehensive diagnosis by collecting and integrating the patient's medical records from multiple medical institutions. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or not. For example, the diagnostic support system can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The diagnostic support system can also collect the patient's past surgical records and treatment history from multiple medical institutions. The diagnostic support system can also integrate the patient's past medical records through an electronic medical record system and store them in a database.

[0072] Diagnostic support systems can improve classification accuracy by considering the interrelationships between data. For example, integrating endoscopic images and CT scan data can improve classification accuracy. It can also integrate endoscopic images with the patient's past examination data to improve classification accuracy. Furthermore, integrating endoscopic images with the patient's medical records can improve classification accuracy. This allows for more accurate diagnoses by improving classification accuracy by considering the interrelationships between data. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or without AI. For example, a diagnostic support system can integrate endoscopic images and CT scan data to improve classification accuracy. A diagnostic support system can also integrate endoscopic images with the patient's past examination data to improve classification accuracy. A diagnostic support system can also integrate endoscopic images with the patient's medical records to improve classification accuracy.

[0073] The diagnostic support system can optimize its prediction algorithm by referring to past prediction data. For example, it can improve the accuracy of the prediction algorithm based on past prediction data. It can also analyze past prediction data and adjust the parameters of the prediction algorithm. Furthermore, it can learn the prediction algorithm by referring to past prediction data. This improves prediction accuracy by optimizing the prediction algorithm by referring to past prediction data. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or without AI. For example, the diagnostic support system can improve the accuracy of its prediction algorithm based on past prediction data. The diagnostic support system can also analyze past prediction data and adjust the parameters of the prediction algorithm. The diagnostic support system can also learn the prediction algorithm by referring to past prediction data.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects endoscopic images, other medical images, and past patient examination data and medical records. For example, it collects images acquired during an endoscopic examination in real time and immediately saves them to the database. It can also collect other medical images (CT scans, MRI, etc.) simultaneously and save them to an integrated database. Furthermore, it can automatically classify the collected image data and save it to the appropriate folder. Step 2: The analysis unit analyzes the data collected by the data acquisition unit to detect and classify lesions and predict their malignancy. For example, it can analyze endoscopic images in real time to detect lesions. It can also analyze the patient's past examination data to assess the progression of the lesion. Furthermore, it can analyze the patient's medical records to identify risk factors for the lesion. Step 3: The classification unit automatically classifies the lesions based on the results obtained by the analysis unit. For example, it analyzes endoscopic images and classifies lesions as benign or malignant. It can also analyze the patient's past examination data and classify based on the progression of the lesion. Furthermore, it can analyze the patient's medical records and classify based on the risk factors of the lesion. Step 4: The prediction unit predicts the malignancy of the lesions classified by the classification unit. For example, it analyzes endoscopic images and predicts malignancy based on the shape and size of the lesion. It can also analyze the patient's past examination data to predict the risk of lesion progression. Furthermore, it can analyze the patient's medical records to predict the risk of lesion recurrence. Step 5: The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. For example, it integrates the patient's past test data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. It can also propose an individualized treatment plan considering the patient's lifestyle data. Furthermore, it can propose an individualized diagnosis and treatment plan considering the patient's current health status.

[0076] (Example of form 2) The diagnostic support system according to an embodiment of the present invention is a system that uses AI to improve diagnostic accuracy in the medical field. This diagnostic support system collects endoscopic images, other medical images, past examination data and medical records of patients, and the AI ​​analyzes them to detect and classify lesions and predict their malignancy. Furthermore, it automatically classifies lesions and predicts their malignancy based on the analysis results. This supports physicians' diagnoses and prevents oversights. In addition, the AI ​​integrates past examination data, medical records, genetic information, etc., and proposes a diagnosis and treatment plan optimized for each individual patient. The system continuously learns and incorporates new cases and the latest research results to constantly improve diagnostic accuracy. Furthermore, it automatically summarizes examination results and generates detailed reports, significantly improving the efficiency of physicians' work. For example, the diagnostic support system collects images in real time during endoscopic examinations, and the AI ​​immediately performs analysis. The analysis results indicate the presence or absence of lesions, their type, and malignancy, and are quickly fed back to the physician. This allows physicians to make immediate diagnoses during examinations. The diagnostic support system also integrates past examination data and medical records of patients, and the AI ​​performs analysis to propose individualized diagnoses and treatment plans. For example, the system considers the patient's genetic information and lifestyle data to suggest the optimal treatment options. This ensures that the most appropriate treatment is provided for each individual patient. Furthermore, the diagnostic support system continuously learns and incorporates new cases and the latest research findings to constantly improve diagnostic accuracy. For instance, if a new lesion pattern or treatment method is discovered, the system learns about it and incorporates it into the next diagnosis. This allows the diagnostic support system to always make diagnoses based on the latest medical knowledge. In this way, the diagnostic support system can support physicians' diagnoses and prevent oversights.

[0077] The diagnostic support system according to this embodiment comprises a data collection unit, an analysis unit, a classification unit, a prediction unit, and a proposal unit. The data collection unit collects endoscopic images, other medical images, and past examination data and medical records of patients. For example, the data collection unit collects images acquired during an endoscopic examination in real time and immediately saves them to a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and save them to an integrated database. Furthermore, the data collection unit can automatically classify the collected image data and save it to an appropriate folder. For example, the data collection unit collects images acquired during an endoscopic examination in real time and immediately saves them to a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and save them to an integrated database. The data collection unit can also automatically classify the collected image data and save it to an appropriate folder. The analysis unit analyzes the data collected by the data collection unit to detect and classify lesions and predict their malignancy. For example, the analysis unit analyzes endoscopic images in real time to detect lesions. The analysis unit can also analyze past examination data of patients to evaluate the progression of lesions. Furthermore, the analysis unit can also analyze the patient's medical records and identify risk factors for lesions. For example, the analysis unit can analyze images acquired during endoscopic examinations in real time to detect lesions. The analysis unit can also analyze the patient's past examination data to assess the progression of lesions. The analysis unit can also analyze the patient's medical records and identify risk factors for lesions. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. For example, the classification unit analyzes endoscopic images and classifies lesions into benign and malignant. The classification unit can also analyze the patient's past examination data and classify lesions based on their progression. Furthermore, the classification unit can analyze the patient's medical records and classify lesions based on risk factors. For example, the classification unit analyzes endoscopic images and classifies lesions into benign and malignant. The classification unit can also analyze the patient's past examination data and classify lesions based on their progression. The classification unit can also analyze the patient's medical records and classify lesions based on risk factors. The prediction unit predicts the malignancy of lesions classified by the classification unit.The prediction unit, for example, analyzes endoscopic images and predicts the degree of malignancy based on the shape and size of the lesion. The prediction unit can also analyze the patient's past examination data and predict the risk of lesion progression. Furthermore, the prediction unit can analyze the patient's medical records and predict the risk of lesion recurrence. For example, the prediction unit analyzes endoscopic images and predicts the degree of malignancy based on the shape and size of the lesion. The prediction unit can also analyze the patient's past examination data and predict the risk of lesion progression. The prediction unit can also analyze the patient's medical records and predict the risk of lesion recurrence. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. For example, the proposal unit integrates the patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. Furthermore, the proposal unit can also propose an individualized treatment plan considering the patient's lifestyle data. Furthermore, the proposal unit can also propose an individualized diagnosis and treatment plan considering the patient's current health status. For example, the proposal unit integrates the patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. The proposal unit can also propose an individualized treatment plan, taking into account the patient's lifestyle data. The proposal unit can also propose an individualized diagnosis and treatment plan, taking into account the patient's current health status. This allows the diagnostic support system according to the embodiment to support the physician's diagnosis and prevent oversights. Some or all of the above-described processes in the collection unit, analysis unit, classification unit, prediction unit, and proposal unit may be performed using AI, for example, or not using AI. For example, the collection unit collects images acquired during endoscopic examination in real time and immediately stores them in a database. The analysis unit analyzes the data collected by the collection unit to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit. The prediction unit predicts the malignancy of the lesions classified by the classification unit. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit.

[0078] The data collection unit collects endoscopic images, other medical images, and past examination data and medical records of patients. Specifically, it collects images acquired during endoscopic examinations in real time and immediately saves them to a database. The endoscopic images are high resolution and designed to ensure that even minute lesions are not missed. The data collection unit can also simultaneously collect other medical images such as CT scans and MRIs and store this data in an integrated database. This allows for centralized management of different types of medical images, making them easily accessible to the analysis and classification units. Furthermore, the data collection unit has the function of automatically classifying the collected image data and saving it to the appropriate folders. For example, endoscopic images are classified into categories such as the digestive system and respiratory system, and CT scans and MRI images are classified into categories such as the brain, chest, and abdomen. This allows for quick retrieval of necessary data and its use in analysis. The data collection unit also collects past examination data and medical records of patients and stores this data in an integrated database. Past examination data includes blood test results, pathology test results, and genetic information, and this data is useful for identifying the progression of lesions and risk factors. The data collection unit automatically updates this data, ensuring that the latest information is always maintained. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, for acutely ill patients, the data collection frequency can be increased, and if a rapid response is required, data can be collected in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0079] The analysis unit analyzes data collected by the data collection unit to detect and classify lesions and predict their malignancy. Specifically, it analyzes endoscopic images in real time to detect lesions. By utilizing AI-based image recognition technology, it can detect minute lesions and abnormalities with high accuracy. For example, it can automatically identify lesions such as polyps and ulcers in endoscopic images and determine their location and size. The analysis unit can also analyze the patient's past examination data to evaluate the progression of lesions. Past examination data includes blood test results and pathology test results, and by statistically analyzing this data, it identifies the rate of lesion progression and risk factors. Furthermore, the analysis unit can analyze the patient's medical records to identify risk factors for lesions. For example, it can analyze the patient's lifestyle and genetic information to evaluate how specific risk factors affect the occurrence and progression of lesions. The analysis unit can integrate and analyze this data to perform a comprehensive risk assessment. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past endoscopic image data, it can predict the incidence and progression of lesions in specific regions and time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0080] The classification unit automatically classifies lesions based on the results obtained by the analysis unit. Specifically, it analyzes endoscopic images and classifies lesions into benign and malignant categories. Utilizing AI-based classification algorithms, it performs highly accurate classification based on characteristics such as shape, color, and texture of the lesions. For example, it analyzes differences in polyp shape and color to automatically identify benign and malignant polyps. The classification unit can also analyze a patient's past examination data and classify lesions based on their progression. This data includes pathology and blood test results. Based on this data, it evaluates the progression and risk of lesions and classifies them into appropriate categories. Furthermore, the classification unit can analyze a patient's medical records and classify lesions based on risk factors. For example, it analyzes a patient's lifestyle and genetic information to evaluate how specific risk factors affect the development and progression of lesions and identify high-risk patients. The classification unit can comprehensively analyze this data to perform a comprehensive risk assessment. Additionally, the classification unit can continuously revise its classification results based on real-time updated data to adapt to the latest situations. For example, if new test data or medical records are added, the classification unit immediately incorporates the new data and updates the classification results. Furthermore, the classification unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the classification unit to not only monitor the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0081] The prediction unit predicts the malignancy of lesions classified by the classification unit. Specifically, it analyzes endoscopic images and predicts malignancy based on the shape and size of the lesion. By utilizing an AI-based prediction algorithm and analyzing the characteristics of the lesion in detail, it performs highly accurate malignancy predictions. For example, it analyzes the shape, size, and color changes of polyps to identify polyps with a high degree of malignancy. The prediction unit can also analyze the patient's past examination data to predict the risk of lesion progression. Past examination data includes pathology test results and blood test results, and based on this data, it evaluates the rate and risk of lesion progression and predicts future risk. Furthermore, the prediction unit can analyze the patient's medical records to predict the risk of lesion recurrence. For example, it analyzes the patient's lifestyle and genetic information to evaluate how specific risk factors affect lesion recurrence and identify patients at high risk of recurrence. The prediction unit can integrate and analyze this data to perform a comprehensive risk assessment. In addition, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if new test data or medical records are added, the prediction unit immediately incorporates the new data and updates the prediction results. Furthermore, the prediction unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the prediction unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0082] The proposal unit proposes personalized diagnoses and treatment plans based on the prediction results obtained by the prediction unit. Specifically, it integrates the patient's past test data, medical records, and genetic information to propose a diagnosis and treatment plan optimized for each individual patient. Utilizing AI-powered diagnostic support algorithms, it develops optimal treatment plans by analyzing the patient's characteristics and risk factors in detail. For example, based on the patient's past test data, it evaluates the effectiveness of a particular treatment and proposes the optimal treatment. The proposal unit can also propose personalized treatment plans considering the patient's lifestyle data. For example, it analyzes the patient's diet and exercise habits, evaluates the impact of lifestyle improvements on treatment, and proposes specific improvement measures. Furthermore, the proposal unit can propose personalized diagnoses and treatment plans considering the patient's current health status. For example, based on the patient's latest test results and medical records, it proposes the most suitable treatment for their current health condition. The proposal unit can comprehensively analyze this data to develop a comprehensive diagnosis and treatment plan. In addition, the proposal unit can continuously modify its proposals based on real-time updated data to adapt to the latest situation. For example, if new test data or medical records are added, the proposal unit immediately incorporates the new data and updates its proposals. Furthermore, the proposed system can use an anomaly detection algorithm to detect unusual patterns and abnormal data, and issue warnings early. This allows the system to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0083] The learning unit can continuously train the generative AI. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. Furthermore, the learning unit can adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. The learning unit can also adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process. This allows the generative AI to continuously learn, improving the accuracy of the system. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can continuously learn from new data using online learning. The learning unit can also periodically update data using batch learning. The learning unit can also adjust the frequency of data updates to ensure that the latest data is always reflected in the learning process.

[0084] The report generation unit can automatically summarize test results and generate a detailed report. For example, the report generation unit can automatically summarize test results using a summarization algorithm. It can also perform summarization based on the length of the summary and the importance of the information being summarized. Furthermore, the report generation unit can adjust the items, format, and depth of information in the detailed report. This improves the efficiency of physicians by automatically summarizing test results and generating a detailed report. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can automatically summarize test results using a summarization algorithm. It can also perform summarization based on the length of the summary and the importance of the information being summarized. The report generation unit can also adjust the items, format, and depth of information in the detailed report.

[0085] The data collection unit can collect endoscopic images and other medical images in real time and store them in a database. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in the database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. Furthermore, the data collection unit can automatically classify the collected image data and save it to the appropriate folder. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. The data collection unit can also automatically classify the collected image data and save it in the appropriate folder. This enables rapid data analysis by collecting endoscopic images and other medical images in real time and storing them in a database. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect images acquired during an endoscopic examination in real time and immediately store them in a database. The data collection unit can also simultaneously collect other medical images (CT scans, MRI, etc.) and store them in an integrated database. The collection unit can also automatically classify the collected image data and save it to the appropriate folder.

[0086] The data collection unit can automatically retrieve past examination data from the electronic medical record system. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the electronic medical record system. Furthermore, the data collection unit can retrieve past blood test results and biometric information from the electronic medical record system and store them in an integrated database. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the electronic medical record system. The data collection unit can also retrieve past blood test results and biometric information from the electronic medical record system and store them in an integrated database. This makes data integration easier by automatically retrieving past examination data from the electronic medical record system. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The data collection unit can also collect past CT scan and MRI data from the patient's electronic medical record system. Furthermore, it can retrieve past blood test results and biometric information from the patient's electronic medical record system and store them in an integrated database.

[0087] The data collection unit can collect and integrate a patient's medical records from multiple medical institutions. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. Furthermore, the data collection unit can integrate a patient's past medical records through an electronic medical record system and store them in a database. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. The data collection unit can also integrate a patient's past medical records through an electronic medical record system and store them in a database. This enables a comprehensive diagnosis by collecting a patient's medical records from multiple medical institutions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The data collection unit can also collect a patient's past surgical records and treatment history from multiple medical institutions. The data collection unit can also integrate patients' past medical records through the electronic medical record system and store them in a database.

[0088] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is relaxed, the data collection unit will collect data during the endoscopic examination. If the patient is tense, the data collection unit can also allow time for relaxation before the examination before collecting data. Furthermore, if the patient is feeling anxious, the data collection unit can also collect data after the examination. This allows for more appropriate data collection by adjusting the timing of data collection according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data during the endoscopic examination if the patient is relaxed. If the patient is anxious, the data collection unit can also provide time for the patient to relax before the examination before collecting data. If the patient is feeling anxious, the data collection unit can also collect data after the examination.

[0089] The data collection unit can collect patients' lifestyle data and integrate and analyze it with medical data. For example, the data collection unit can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The data collection unit can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. Furthermore, the data collection unit can collect patients' smoking and drinking habits and integrate and analyze them with medical data. This allows for a more comprehensive diagnosis by collecting patients' lifestyle data and integrating and analyzing it with medical data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The data collection unit can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. The data collection unit can also collect patients' smoking and drinking habits and integrate and analyze them with medical data.

[0090] The data collection unit can filter data based on the patient's current health status and past treatment history. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. Furthermore, the data collection unit can filter and collect specific data based on the patient's current symptoms. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. The data collection unit can also filter and collect specific data based on the patient's current symptoms. This allows for the efficient collection of only the necessary data by filtering data based on the patient's current health status and past treatment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect only the necessary data based on the patient's current health status. The data collection unit can also prioritize the collection of highly relevant data based on the patient's past treatment history. The data collection unit can also filter and collect specific data based on the patient's current symptoms.

[0091] The analysis unit can analyze endoscopic images in real time and detect lesions. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. Furthermore, the analysis unit can save the analysis results of the endoscopic images to a database for later reference. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. The analysis unit can also save the analysis results of the endoscopic images to a database for later reference. This enables rapid diagnosis by analyzing endoscopic images in real time and detecting lesions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze images acquired during an endoscopic examination in real time and detect lesions. The analysis unit can also immediately provide feedback on the analysis results of the endoscopic images to the physician. The analysis unit can also save the analysis results of the endoscopic images to a database for later reference.

[0092] The analysis unit can analyze a patient's past examination data and evaluate the progression of the disease. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. Furthermore, the analysis unit can analyze a patient's past blood test results and evaluate the progression of the disease. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past blood test results and evaluate the progression of the disease. This allows for the development of an appropriate treatment plan by analyzing a patient's past examination data and evaluating the progression of the disease. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze a patient's past endoscopic examination data and evaluate the progression of the disease. The analysis unit can also analyze a patient's past CT scan and MRI data and evaluate the progression of the disease. The analysis unit can also analyze the patient's past blood test results to assess the progression of the disease.

[0093] The analysis unit can analyze a patient's medical records and identify risk factors for lesions. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. Furthermore, the analysis unit can analyze a patient's lifestyle data and identify risk factors for lesions. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. The analysis unit can also analyze a patient's lifestyle data and identify risk factors for lesions. This allows for preventative treatment by analyzing a patient's medical records and identifying risk factors for lesions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze a patient's past medical records and identify risk factors for lesions. The analysis unit can also analyze a patient's genetic information and identify risk factors for lesions. The analysis unit can also analyze a patient's lifestyle data and identify risk factors for lesions.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. It can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows for more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can provide simple and easy-to-understand results when the user is stressed. It can also provide detailed results when the user is relaxed. And when the user is in a hurry, it can provide concise and to-the-point results.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data.

[0096] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a specific analysis algorithm to endoscopic images. The analysis unit can also apply different analysis algorithms to CT scan and MRI data. Furthermore, the analysis unit can apply specific analysis algorithms to blood test results. This allows for more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply a specific analysis algorithm to endoscopic images. The analysis unit can also apply different analysis algorithms to CT scan and MRI data. The analysis unit can also apply specific analysis algorithms to blood test results.

[0097] The classification unit can analyze endoscopic images and automatically classify lesions. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). Furthermore, the classification unit can analyze endoscopic images and classify lesions based on their size and shape. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). This enables rapid diagnosis by analyzing endoscopic images and automatically classifying lesions. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze endoscopic images and classify lesions into benign and malignant types. The classification unit can also analyze endoscopic images and classify lesions by type (polyp, ulcer, etc.). The classification unit can also analyze endoscopic images and classify lesions based on their size and shape.

[0098] The classification unit can analyze a patient's past examination data and classify them based on the progression of the disease. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past CT scan and MRI data and classify them based on the progression of the disease. Furthermore, the classification unit can analyze a patient's past blood test results and classify them based on the progression of the disease. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past CT scan and MRI data and classify them based on the progression of the disease. The classification unit can also analyze a patient's past blood test results and classify them based on the progression of the disease. This allows for the creation of an appropriate treatment plan by analyzing a patient's past examination data and classifying them based on the progression of the disease. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze a patient's past endoscopic examination data and classify them based on the progression of the disease. The classification unit can also analyze past CT scan and MRI data of patients and classify them based on the progression of their lesions. The classification unit can also analyze past blood test results of patients and classify them based on the progression of their lesions.

[0099] The classification unit can analyze a patient's medical records and classify them based on the risk factors for lesions. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. Furthermore, the classification unit can analyze a patient's lifestyle data and classify it based on the risk factors for lesions. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. The classification unit can also analyze a patient's lifestyle data and classify it based on the risk factors for lesions. This makes preventive treatment possible by analyzing a patient's medical records and classifying them based on the risk factors for lesions. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can analyze a patient's past medical records and classify them based on the risk factors for lesions. The classification unit can also analyze a patient's genetic information and classify it based on the risk factors for lesions. The classification unit can also analyze a patient's lifestyle data and classify it based on the risk factors for lesions.

[0100] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, if the user is nervous, the classification unit provides simple and easily understandable classification criteria. It can also provide detailed classification criteria if the user is relaxed. Furthermore, if the user is in a hurry, the classification unit can provide concise classification criteria. This allows for more appropriate classification results by adjusting the classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, if the user is nervous, the classification unit provides simple and easily understandable classification criteria. The classification unit can provide detailed classification criteria when the user is relaxed. It can also provide concise classification criteria when the user is in a hurry.

[0101] The classification unit can improve the accuracy of classification by considering the interrelationships between data. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. Furthermore, the classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's medical records. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's medical records. This allows for more accurate diagnoses by improving the accuracy of classification by considering the interrelationships between data. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can improve the accuracy of classification by integrating endoscopic images and CT scan data. The classification unit can also improve the accuracy of classification by integrating endoscopic images and the patient's past examination data. The classification unit can also integrate endoscopic images and patient medical records to improve the accuracy of classification.

[0102] The classification unit can perform classification by considering the attribute information of the data submitter. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. Furthermore, the classification unit can classify data based on the patient's age and gender. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. The classification unit can also classify data based on the patient's age and gender. This allows for more appropriate classification by considering the attribute information of the data submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can classify data based on the physician's specialty. The classification unit can also classify data based on the type of medical institution. The classification unit can also classify data based on the patient's age and gender.

[0103] The prediction unit can analyze endoscopic images and predict the malignancy of a lesion. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. Furthermore, the prediction unit can analyze endoscopic images and predict the malignancy based on the location of the lesion and its relationship to surrounding tissue. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. It can also analyze endoscopic images and predict the malignancy based on the location of the lesion and its relationship to surrounding tissue. This enables rapid diagnosis by analyzing endoscopic images and predicting the malignancy of the lesion. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze endoscopic images and predict the malignancy based on the shape and size of the lesion. It can also analyze endoscopic images and predict the malignancy based on the color and texture of the lesion. The prediction unit can also analyze endoscopic images and predict the degree of malignancy based on the location of the lesion and its relationship to surrounding tissue.

[0104] The prediction unit can analyze a patient's past examination data and predict the risk of disease progression. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. Furthermore, the prediction unit can analyze a patient's past blood test results to predict the risk of disease progression. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. The prediction unit can also analyze a patient's past blood test results to predict the risk of disease progression. This allows for the development of an appropriate treatment plan by analyzing a patient's past examination data and predicting the risk of disease progression. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze a patient's past endoscopic examination data to predict the risk of disease progression. The prediction unit can also analyze a patient's past CT scan and MRI data to predict the risk of disease progression. The prediction unit can also analyze the patient's past blood test results to predict the risk of disease progression.

[0105] The prediction unit can analyze a patient's medical records and predict the risk of lesion recurrence. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. Furthermore, the prediction unit can analyze a patient's lifestyle data and predict the risk of lesion recurrence. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's lifestyle data and predict the risk of lesion recurrence. This makes preventive treatment possible by analyzing a patient's medical records and predicting the risk of lesion recurrence. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can analyze a patient's past medical records and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's genetic information and predict the risk of lesion recurrence. The prediction unit can also analyze a patient's lifestyle data and predict the risk of lesion recurrence.

[0106] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is nervous, the prediction unit provides a simple and easy-to-understand prediction result. It can also provide a more detailed prediction result if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can provide a concise prediction result. This allows for more appropriate prediction results by adjusting the prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, if the user is nervous, the prediction unit provides a simple and easy-to-understand prediction result. The prediction unit can provide detailed prediction results when the user is relaxed. It can also provide concise prediction results when the user is in a hurry.

[0107] The prediction unit can optimize the prediction algorithm by referring to past prediction data. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. Furthermore, the prediction unit can learn the prediction algorithm by referring to past prediction data. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. The prediction unit can also learn the prediction algorithm by referring to past prediction data. This improves prediction accuracy by optimizing the prediction algorithm by referring to past prediction data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can improve the accuracy of the prediction algorithm based on past prediction data. The prediction unit can also analyze past prediction data and adjust the parameters of the prediction algorithm. The prediction unit can also learn the prediction algorithm by referring to past prediction data.

[0108] The prediction unit can apply different prediction methods to each data category. For example, the prediction unit can apply a specific prediction method to endoscopic images. It can also apply different prediction methods to CT scan and MRI data. Furthermore, the prediction unit can apply specific prediction methods to blood test results. This allows for more accurate predictions by applying different prediction methods to each data category. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can apply a specific prediction method to endoscopic images. It can also apply different prediction methods to CT scan and MRI data. It can also apply specific prediction methods to blood test results.

[0109] The suggestion unit can integrate a patient's past examination data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. It can also propose an individualized diagnosis based on a patient's genetic information. Furthermore, the suggestion unit can integrate a patient's medical records to propose an optimal treatment plan. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. The suggestion unit can also propose an individualized diagnosis based on a patient's genetic information. The suggestion unit can also integrate a patient's medical records to propose an optimal treatment plan. This allows for the proposal of an individualized diagnosis and treatment plan by integrating a patient's past examination data, medical records, genetic information, etc. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can propose an optimal treatment plan based on a patient's past endoscopic examination data. The suggestion unit can also propose an individualized diagnosis based on a patient's genetic information. The suggestion unit can also integrate a patient's medical records to propose an optimal treatment plan.

[0110] The suggestion unit can propose an individualized treatment plan by considering the patient's lifestyle data. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. Furthermore, the suggestion unit can propose an optimal treatment plan based on the patient's smoking and drinking habits. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. The suggestion unit can also propose an optimal treatment plan based on the patient's smoking and drinking habits. This allows for the proposal of a more appropriate treatment plan by considering the patient's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can propose an optimal treatment plan based on the patient's dietary records and exercise habits. The suggestion unit can also propose an individualized treatment plan by considering the patient's sleep patterns and stress levels. The suggestion unit can also propose an optimal treatment plan based on the patient's smoking and drinking habits.

[0111] The suggestion unit can propose an individualized diagnosis and treatment plan, taking into account the patient's current health condition. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. Furthermore, the suggestion unit can propose an optimal treatment plan based on the patient's current treatment history. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. The suggestion unit can also propose an optimal treatment plan, taking into account the patient's current treatment history. This allows for the proposal of a more appropriate diagnosis and treatment plan by considering the patient's current health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can propose an optimal diagnosis and treatment plan based on the patient's current health condition. The suggestion unit can also propose an individualized diagnosis, taking into account the patient's current symptoms. The suggestion unit can also propose an optimal treatment plan, taking into account the patient's current treatment history.

[0112] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion section can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion section can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion section can provide concise suggestions. For example, if the user is nervous, the suggestion section can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion section can also provide detailed suggestions. If the user is in a hurry, the suggestion section can also provide concise suggestions. This allows for the provision of more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, if the user is nervous, the suggestion section can provide simple and easy-to-understand suggestions. The proposal team can provide detailed suggestions if the user is relaxed. They can also provide concise suggestions if the user is in a hurry.

[0113] The suggestion unit can adjust the level of detail of its suggestions based on the patient's importance. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. Furthermore, the suggestion unit can prioritize suggestions according to the patient's importance. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. The suggestion unit can also prioritize suggestions according to the patient's importance. This allows for efficient suggestions by adjusting the level of detail of suggestions based on the patient's importance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can provide detailed suggestions to high-priority patients. It can also provide simplified suggestions to low-priority patients. The suggestion unit can also prioritize suggestions according to the patient's importance.

[0114] The suggestion unit can apply different suggestion algorithms depending on the patient category. For example, the suggestion unit can apply a specific suggestion algorithm to patients who have undergone an endoscopy. Furthermore, the suggestion unit can apply a different suggestion algorithm to patients who have CT scan or MRI data. Additionally, the suggestion unit can apply a specific suggestion algorithm to patients who have blood test results. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the patient category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can apply a specific suggestion algorithm to patients who have undergone an endoscopy. The suggestion unit can also apply a different suggestion algorithm to patients who have CT scan or MRI data. The suggestion unit can also apply a specific suggestion algorithm to patients who have blood test results.

[0115] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. It can also select simpler training data if the user is tense. Furthermore, if the user is in a hurry, the learning unit can select concise training data. This allows for more effective learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, if the user is relaxed, the learning unit will select detailed training data. The learning unit can also select simple training data if the user is feeling stressed. The learning unit can also select concise training data if the user is in a hurry.

[0116] The learning unit can optimize the learning algorithm by referring to past training data. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can train the learning algorithm by referring to past training data. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. The learning unit can also train the learning algorithm by referring to past training data. This improves learning accuracy by optimizing the learning algorithm by referring to past training data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can improve the accuracy of the learning algorithm based on past training data. The learning unit can also analyze past training data and adjust the parameters of the learning algorithm. The learning unit can also train the learning algorithm by referring to past training data.

[0117] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is stressed. Furthermore, the learning unit can adjust the learning frequency when the user is in a hurry. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is stressed. It can also adjust the learning frequency when the user is in a hurry. This allows for more effective learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is stressed. The learning section also allows users to adjust the learning frequency if they are in a hurry.

[0118] The learning unit can weight the training data based on when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. Furthermore, the learning unit can adjust the weighting of the training data according to when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. The learning unit can also adjust the weighting of the training data according to when the data was collected. This allows for more effective learning by weighting the training data based on when the data was collected. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to older data. The learning unit can also adjust the weighting of the training data according to when the data was collected.

[0119] The report generation unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is nervous, the report generation unit can provide a simple and easy-to-read report. If the user is relaxed, the report generation unit can also provide a detailed report. Furthermore, if the user is in a hurry, the report generation unit can provide a concise report. For example, if the user is nervous, the report generation unit can provide a simple and easy-to-read report. If the user is relaxed, the report generation unit can also provide a detailed report. If the user is in a hurry, the report generation unit can also provide a concise report. This allows for the provision of more appropriate reports by adjusting the presentation of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide a simple and easy-to-understand report when the user is stressed. It can also provide a detailed report when the user is relaxed. And when the user is in a hurry, it can provide a concise report.

[0120] The report generation unit can adjust the level of detail in the report based on the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. Furthermore, the report generation unit can determine the priority of reports according to the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. It can also determine the priority of reports according to the importance of the data. This allows for efficient report generation by adjusting the level of detail in the report based on the importance of the data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. It can also determine the priority of reports according to the importance of the data.

[0121] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is nervous, the report generation unit can provide a short, concise report. If the user is relaxed, the report generation unit can also provide a longer report with more detailed explanations. Furthermore, if the user is in a hurry, the report generation unit can provide a brief report. For example, if the user is nervous, the report generation unit can provide a short, concise report. If the user is relaxed, the report generation unit can also provide a longer report with more detailed explanations. If the user is in a hurry, the report generation unit can also provide a brief report. This allows for the provision of more appropriate reports by adjusting the length of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI, for example, or without AI. For example, if the user is stressed, the report generator will provide a short, to-the-point report. If the user is relaxed, the report generator can also provide a longer report with detailed explanations. If the user is in a hurry, the report generator can also provide a concise report.

[0122] The report generation unit can determine the priority of reports based on the data collection timing. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. Furthermore, the report generation unit can determine the priority of reports according to the data collection timing. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. The report generation unit can also determine the priority of reports according to the data collection timing. This enables efficient report generation by determining the priority of reports based on the data collection timing. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without using AI. For example, the report generation unit can determine the priority of reports based on the latest data. The report generation unit can also adjust the priority of reports based on older data. The report generation unit can also determine the priority of reports according to the data collection timing.

[0123] The report generation unit can adjust the level of detail in a report based on the importance of the data during report generation. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. Furthermore, the report generation unit can determine the priority of reports according to the importance of the data. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. The report generation unit can also determine the priority of reports according to the importance of the data. This allows for efficient report generation by adjusting the level of detail in reports based on the importance of the data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate detailed reports for highly important data. It can also generate simplified reports for less important data. The report generation unit can also determine the priority of reports according to the importance of the data.

[0124] The report generation unit can adjust the report content when generating a report, taking into account the user's current health status. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. Furthermore, the report generation unit can optimize the report content based on the user's current treatment history. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. The report generation unit can also optimize the report content based on the user's current treatment history. This allows for the provision of more appropriate reports by adjusting the report content to take into account the user's current health status. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide optimal report content based on the user's current health status. The report generation unit can also adjust the report content considering the user's current symptoms. The report generation unit can also optimize the report content based on the user's current treatment history.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The diagnostic support system can estimate the patient's emotions and adjust the feedback method of the diagnostic results based on the estimated emotions. For example, if the patient is feeling anxious, the diagnostic results can be provided in simple, reassuring language. If the patient is relaxed, the diagnostic results can include detailed explanations. Furthermore, if the patient is in a hurry, the diagnostic results can be provided in a concise, to-the-point manner. This allows for the provision of more appropriate diagnostic results by adjusting the feedback method of the diagnostic results according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or not using AI. For example, if the diagnostic support system is feeling anxious, the diagnostic results can be provided in simple, reassuring language. If the patient is relaxed, the diagnostic support system can also provide diagnostic results that include detailed explanations. If the patient is in a hurry, the diagnostic support system can also provide a concise, to-the-point diagnostic result.

[0127] A diagnostic support system can collect patients' lifestyle data and integrate and analyze it with medical data. For example, it can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. It can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. Furthermore, it can collect patients' smoking and drinking habits and integrate and analyze them with medical data. This allows for a more comprehensive diagnosis by collecting patients' lifestyle data and integrating and analyzing it with medical data. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or not. For example, the diagnostic support system can collect patients' dietary records and exercise habits and integrate and analyze them with medical data. The diagnostic support system can also collect patients' sleep patterns and stress levels and integrate and analyze them with medical data. The diagnostic support system can also collect patients' smoking and drinking habits and integrate and analyze them with medical data.

[0128] The diagnostic support system can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is relaxed, data collection can be performed during the endoscopic examination. If the patient is anxious, time can be provided for relaxation before the examination before data collection. Furthermore, if the patient is feeling anxious, data collection can be performed after the examination. By adjusting the timing of data collection according to the patient's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or without AI. For example, if the diagnostic support system is relaxed, data collection can be performed during the endoscopic examination. If the patient is anxious, data can be provided for relaxation before the examination before data collection. If the patient is feeling anxious, data can be collected after the examination.

[0129] The diagnostic support system can automatically retrieve a patient's past examination data from the electronic medical record system. For example, it can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. It can also collect the patient's past CT scan and MRI data from the electronic medical record system. Furthermore, it can retrieve the patient's past blood test results and biometric information from the electronic medical record system and store them in an integrated database. This makes data integration easier by automatically retrieving the patient's past examination data from the electronic medical record system. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or without AI. For example, the diagnostic support system can access the patient's electronic medical record system and automatically retrieve past endoscopic examination data. The diagnostic support system can also collect the patient's past CT scan and MRI data from the electronic medical record system. The diagnostic support system can also retrieve the patient's past blood test results and biometric information from the electronic medical record system and store them in an integrated database.

[0130] The diagnostic support system can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is tense, it can provide simple and easy-to-understand analysis results. If the patient is relaxed, it can also provide detailed analysis results. Furthermore, if the patient is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the patient's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or not using AI. For example, the diagnostic support system provides simple and easy-to-understand analysis results if the patient is tense. The diagnostic support system can also provide detailed analysis results if the patient is relaxed. The diagnostic support system can also provide concise analysis results if the patient is in a hurry.

[0131] A diagnostic support system can collect and integrate a patient's medical records from multiple medical institutions. For example, it can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. It can also collect the patient's past surgical records and treatment history from multiple medical institutions. Furthermore, it can integrate the patient's past medical records through an electronic medical record system and store them in a database. This enables a comprehensive diagnosis by collecting and integrating the patient's medical records from multiple medical institutions. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or not. For example, the diagnostic support system can collect medical records from multiple medical institutions the patient has visited in the past and store them in an integrated database. The diagnostic support system can also collect the patient's past surgical records and treatment history from multiple medical institutions. The diagnostic support system can also integrate the patient's past medical records through an electronic medical record system and store them in a database.

[0132] The diagnostic support system can estimate a patient's emotions and adjust classification criteria based on those estimated emotions. For example, if a patient is anxious, it can provide simple and easily understandable classification criteria. If a patient is relaxed, it can provide more detailed classification criteria. Furthermore, if a patient is in a hurry, it can provide concise classification criteria. By adjusting the classification criteria according to the patient's emotions, it can provide more appropriate classification results. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or not using AI. For example, the diagnostic support system provides simple and easily understandable classification criteria if a patient is anxious. The diagnostic support system can also provide detailed classification criteria if a patient is relaxed. The diagnostic support system can also provide concise classification criteria if a patient is in a hurry.

[0133] Diagnostic support systems can improve classification accuracy by considering the interrelationships between data. For example, integrating endoscopic images and CT scan data can improve classification accuracy. It can also integrate endoscopic images with the patient's past examination data to improve classification accuracy. Furthermore, integrating endoscopic images with the patient's medical records can improve classification accuracy. This allows for more accurate diagnoses by improving classification accuracy by considering the interrelationships between data. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or without AI. For example, a diagnostic support system can integrate endoscopic images and CT scan data to improve classification accuracy. A diagnostic support system can also integrate endoscopic images with the patient's past examination data to improve classification accuracy. A diagnostic support system can also integrate endoscopic images with the patient's medical records to improve classification accuracy.

[0134] The diagnostic support system can estimate the patient's emotions and adjust its prediction method based on the estimated emotions. For example, if the patient is anxious, it can provide a simple and easy-to-understand prediction result. If the patient is relaxed, it can also provide a detailed prediction result. Furthermore, if the patient is in a hurry, it can provide a concise prediction result. By adjusting the prediction method according to the patient's emotions, it is possible to provide more appropriate prediction results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic support system may be performed using AI, for example, or not using AI. For example, the diagnostic support system provides a simple and easy-to-understand prediction result if the patient is anxious. The diagnostic support system can also provide a detailed prediction result if the patient is relaxed. The diagnostic support system can also provide a concise prediction result if the patient is in a hurry.

[0135] The diagnostic support system can optimize its prediction algorithm by referring to past prediction data. For example, it can improve the accuracy of the prediction algorithm based on past prediction data. It can also analyze past prediction data and adjust the parameters of the prediction algorithm. Furthermore, it can learn the prediction algorithm by referring to past prediction data. This improves prediction accuracy by optimizing the prediction algorithm by referring to past prediction data. Some or all of the above processes in the diagnostic support system may be performed using AI, for example, or without AI. For example, the diagnostic support system can improve the accuracy of its prediction algorithm based on past prediction data. The diagnostic support system can also analyze past prediction data and adjust the parameters of the prediction algorithm. The diagnostic support system can also learn the prediction algorithm by referring to past prediction data.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The data collection unit collects endoscopic images, other medical images, and past patient examination data and medical records. For example, it collects images acquired during an endoscopic examination in real time and immediately saves them to the database. It can also collect other medical images (CT scans, MRI, etc.) simultaneously and save them to an integrated database. Furthermore, it can automatically classify the collected image data and save it to the appropriate folder. Step 2: The analysis unit analyzes the data collected by the data acquisition unit to detect and classify lesions and predict their malignancy. For example, it can analyze endoscopic images in real time to detect lesions. It can also analyze the patient's past examination data to assess the progression of the lesion. Furthermore, it can analyze the patient's medical records to identify risk factors for the lesion. Step 3: The classification unit automatically classifies the lesions based on the results obtained by the analysis unit. For example, it analyzes endoscopic images and classifies lesions as benign or malignant. It can also analyze the patient's past examination data and classify based on the progression of the lesion. Furthermore, it can analyze the patient's medical records and classify based on the risk factors of the lesion. Step 4: The prediction unit predicts the malignancy of the lesions classified by the classification unit. For example, it analyzes endoscopic images and predicts malignancy based on the shape and size of the lesion. It can also analyze the patient's past examination data to predict the risk of lesion progression. Furthermore, it can analyze the patient's medical records to predict the risk of lesion recurrence. Step 5: The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. For example, it integrates the patient's past test data, medical records, genetic information, etc., to propose a diagnosis and treatment plan optimized for each individual patient. It can also propose an individualized treatment plan considering the patient's lifestyle data. Furthermore, it can propose an individualized diagnosis and treatment plan considering the patient's current health status.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, classification unit, prediction unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects endoscopic images and other medical images using the camera 42 and communication I / F 44 of the smart device 14 and stores them in the database 24 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit and is implemented by the identification processing unit 290 of the data processing unit 12. The prediction unit predicts the malignancy of lesions classified by the classification unit and is implemented by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit and is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, classification unit, prediction unit, and proposal unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects endoscopic images and other medical images using the camera 42 and communication I / F 44 of the smart glasses 214 and stores them in the database 24 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit and is implemented by the identification processing unit 290 of the data processing unit 12. The prediction unit predicts the malignancy of lesions classified by the classification unit and is implemented by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit and is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the data collection unit, analysis unit, classification unit, prediction unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects endoscopic images and other medical images using the camera 42 and communication I / F 44 of the headset terminal 314 and stores them in the database 24 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit and is implemented by the identification processing unit 290 of the data processing unit 12. The prediction unit predicts the malignancy of lesions classified by the classification unit and is implemented by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit and is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the acquisition unit, analysis unit, classification unit, prediction unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 and communication I / F 44 of the robot 414 to collect endoscopic images and other medical images and stores them in the database 24 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to detect and classify lesions and predict their malignancy. The classification unit automatically classifies lesions based on the results obtained by the analysis unit and is implemented by the identification processing unit 290 of the data processing unit 12. The prediction unit predicts the malignancy of lesions classified by the classification unit and is implemented by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit and is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) The collection unit collects endoscopic images and other medical images, as well as past examination data and medical records of patients. The data collected by the aforementioned collection unit is analyzed by an analysis unit that detects and classifies lesions and predicts the degree of malignancy, A classification unit that automatically classifies lesions based on the results obtained by the analysis unit, A prediction unit that predicts the degree of malignancy of lesions classified by the classification unit, The system includes a proposal unit that proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. A system characterized by the following features. (Note 2) The system further comprises a learning unit that continuously learns the aforementioned generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a report generation unit that automatically summarizes inspection results and generates a detailed report. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect endoscopic images and other medical images in real time and store them in a database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system automatically retrieves the patient's past test data from the electronic medical record system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Integrate and collect patient medical records from multiple healthcare institutions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Collect patients' lifestyle data and integrate and analyze it with medical data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Endoscopic images are analyzed in real time to detect lesions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We analyze the patient's past test data to evaluate the progression of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Analyze patient medical records to identify risk factors for lesions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is Analyze endoscopic images and automatically classify lesions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is The patient's past test data is analyzed and classified based on the progression of the disease. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is Analyze patients' medical records and classify them based on the risk factors of the lesions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned classification unit is Improve classification accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned classification unit is Classification is performed taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, Analyzing endoscopic images to predict the malignancy of lesions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, Analyzing the patient's past test data to predict the risk of disease progression. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, Analyze patient medical records to predict the risk of lesion recurrence. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, Optimize the prediction algorithm by referring to past prediction data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, Apply different prediction methods to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, We integrate patients' past test data, medical records, and genetic information to propose personalized diagnostic and treatment plans. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We propose an individualized treatment plan, taking into account the patient's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, We propose an individualized diagnosis and treatment plan, taking into account the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, Adjust the level of detail of the proposal based on the patient's importance. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, Apply different proposed algorithms to patients depending on their category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned learning unit, Optimize the learning algorithm by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned learning unit, Weight the training data based on when the data was collected. The system described in Appendix 2, characterized by the features described herein. (Note 38) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The report generation unit, Adjust the level of detail in the report based on the importance of the data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The report generation unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The report generation unit, Prioritize reports based on when the data was collected. The system described in Appendix 3, characterized by the features described herein. (Note 42) The report generation unit, When generating a report, adjust the level of detail in the report based on the importance of the data. The system described in Appendix 3, characterized by the features described herein. (Note 43) The report generation unit, When generating reports, the report content is adjusted to take into account the user's current health status. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects endoscopic images and other medical images, as well as past examination data and medical records of patients. The data collected by the aforementioned collection unit is analyzed by an analysis unit that detects and classifies lesions and predicts the degree of malignancy, A classification unit that automatically classifies lesions based on the results obtained by the analysis unit, A prediction unit that predicts the degree of malignancy of lesions classified by the classification unit, The system includes a proposal unit that proposes an individualized diagnosis and treatment plan based on the prediction results obtained by the prediction unit. A system characterized by the following features.

2. It also includes a learning unit that continuously trains the generative AI. The system according to feature 1.

3. It includes a report generation unit that automatically summarizes inspection results and generates a detailed report. The system according to feature 1.

4. The aforementioned collection unit is Collect endoscopic images and other medical images in real time and store them in a database. The system according to feature 1.

5. The aforementioned collection unit is The system automatically retrieves the patient's past test data from the electronic medical record system. The system according to feature 1.

6. The aforementioned collection unit is Integrate and collect patient medical records from multiple healthcare institutions. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Collect patients' lifestyle data and integrate and analyze it with medical data. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and past treatment history. The system according to feature 1.

10. The aforementioned analysis unit, Endoscopic images are analyzed in real time to detect lesions. The system according to feature 1.

Images