system

Abstract

The system according to this embodiment aims to improve the accuracy of AI-based image diagnosis by accurately labeling medical image data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a labeling unit, and a learning unit. The data collection unit collects medical image data. The analysis unit analyzes the data collected by the data collection unit. The labeling unit assigns accurate labels based on the analysis results obtained by the analysis unit. The learning unit performs learning based on the data labeled by the labeling 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 accuracy of image diagnosis by AI is low because the labeling of medical image data is not accurate.

[0005] The system according to the embodiment aims to perform accurate labeling on medical image data and improve the accuracy of image diagnosis by AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a labeling unit, and a learning unit. The acquisition unit acquires medical image data. The analysis unit analyzes the data acquired by the acquisition unit. The labeling unit assigns accurate labels based on the analysis results obtained by the analysis unit. The learning unit performs learning based on the data labeled by the labeling unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately label medical image data and improve the accuracy of AI-based image diagnosis. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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). [[ID=第十九]]

[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 system according to an embodiment of the present invention is a system that establishes true image diagnosis by setting up a specialized medical team dedicated to labeling and classification work for AI, and proceeding with classification and data labeling. In this system, the specialized medical team collects medical image data, and the AI ​​analyzes that data. Next, the specialized medical team assigns accurate labels based on the AI's analysis results. This labeling work is used as training data for the AI, improving the AI's diagnostic accuracy. Ultimately, the AI ​​becomes able to perform highly accurate image diagnosis. For example, the specialized medical team collects medical image data. In this case, various types of medical images (e.g., X-ray images, MRI images, CT images, etc.) are collected. This allows the AI ​​to learn from diverse data. Next, the AI ​​analyzes the collected medical image data. The AI ​​uses image recognition technology to detect abnormalities in the images. For example, the AI ​​can analyze an X-ray image and detect a lung nodule. After that, the specialized medical team assigns accurate labels based on the AI's analysis results. This labeling work is performed based on the knowledge and experience of the specialized medical team. For example, the specialized medical team determines whether the nodule detected by the AI ​​is benign or malignant and assigns an appropriate label. Labeled data is used as training data for AI. The AI ​​learns from this labeled data to improve its diagnostic accuracy. For example, by learning from labeled nodule data, the AI ​​will be able to determine with high accuracy whether nodules are benign or malignant when analyzing new X-ray images in the future. Ultimately, the AI ​​will be able to perform highly accurate image diagnoses. This will improve diagnostic accuracy in medical settings and enable early detection and treatment of patients. For example, if the AI ​​can detect lung nodules with high accuracy and start treatment early, the patient's prognosis will improve. This allows the system to efficiently collect, analyze, label, and train on medical image data.

[0029] The image diagnostic system according to this embodiment comprises an acquisition unit, an analysis unit, a labeling unit, and a learning unit. The acquisition unit collects medical image data. Medical image data includes, but is not limited to, X-ray images, MRI images, and CT images. The acquisition unit, for example, takes X-ray images and stores them as digital data. The acquisition unit can also acquire MRI images and CT images and store them as digital data. Furthermore, the acquisition unit can automate the collection of medical image data using AI. For example, the acquisition unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. The analysis unit analyzes the data collected by the acquisition unit. The analysis unit can, for example, use image recognition technology to detect abnormalities in medical images. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI images and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. The labeling unit assigns accurate labels based on the analysis results obtained by the analysis unit. The labeling unit assigns accurate labels to the AI ​​analysis results based on, for example, the knowledge and experience of a specialized medical team. For example, the labeling unit determines whether the lung nodule detected by the AI ​​is benign or malignant and assigns an appropriate label. The learning unit performs learning based on the data labeled by the labeling unit. For example, the learning unit uses AI to learn from the labeled data and improve diagnostic accuracy. For example, the learning unit uses AI to learn from the data of labeled lung nodules, enabling it to determine with high accuracy whether nodules are benign or malignant when analyzing new X-ray images in the future. As a result, the image diagnostic system according to this embodiment can efficiently collect, analyze, label, and learn medical image data.

[0030] The data collection unit collects medical image data. This includes, but is not limited to, X-ray images, MRI images, and CT images. For example, the unit can take X-ray images and save them as digital data. It can also acquire MRI and CT images and save them as digital data. Furthermore, the data collection unit can automate the collection of medical image data using AI. For example, the unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. Specifically, the AI ​​analyzes the patient's electronic medical record and past medical records to identify necessary imaging tests based on specific symptoms and diagnoses. For example, if a lung abnormality is suspected, the AI ​​automatically instructs an X-ray and collects the image data. It can also link with MRI and CT appointment management systems to acquire images at the optimal time based on the patient's schedule. Furthermore, the data collection unit has a function to evaluate the quality of image data in real time and instruct re-shooting if inappropriate images are collected. This allows the data collection unit to efficiently collect high-quality medical image data and improve the accuracy of diagnosis.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use image recognition technology to detect abnormalities in medical images. For instance, it can use AI to detect lung nodules in X-ray images. It can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, it can use AI to analyze medical image data in real time and detect abnormalities. Specifically, the AI ​​uses deep learning technology to learn from a vast dataset of medical images, improving the accuracy of abnormality detection. For example, to detect lung nodules, the AI ​​learns from thousands of X-ray images and recognizes the characteristics of the nodules with high accuracy. Similarly, in MRI and CT images, the AI ​​uses a multi-layer neural network to analyze and detect brain tumors and visceral abnormalities with high accuracy. In addition to detecting abnormalities, the analysis unit can also provide detailed information such as the type, location, and size of the abnormality. This allows physicians to make quick and accurate diagnoses and appropriately determine treatment plans for patients.

[0032] The labeling unit applies accurate labels based on the analysis results obtained by the analysis unit. For example, the labeling unit applies accurate labels to the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit determines whether a lung nodule detected by the AI ​​is benign or malignant and applies an appropriate label. Specifically, the labeling unit allows specialists to review the AI ​​analysis results and apply labels according to the type and severity of the abnormality. For example, if the lung nodule is benign, it will be labeled "benign nodule," and if it is malignant, it will be labeled "malignant nodule." The labeling unit can also add detailed information such as the location, size, and shape of the abnormality as part of the label. Furthermore, the labeling unit integrates the AI ​​analysis results and the specialist's judgment to build a feedback loop that improves the accuracy of the labels. This enables the labeling unit to achieve highly accurate labeling based on the AI ​​analysis results and improve the reliability of the diagnosis.

[0033] The learning unit learns based on data labeled by the labeling unit. For example, the learning unit uses AI to learn from the labeled data and improve diagnostic accuracy. For instance, the learning unit uses AI to learn from labeled lung nodule data, enabling it to accurately determine whether nodules are benign or malignant when analyzing new X-ray images in the future. Specifically, the learning unit iteratively learns from labeled medical image data using deep learning algorithms. For example, it uses a dataset of lung nodules to extract nodule features and build a model for determining benign / malignant nodules. Furthermore, the learning unit can integrate and learn from different types of medical image data (X-ray, MRI, CT, etc.) to increase the versatility of anomaly detection. In addition, the learning unit continuously improves diagnostic accuracy by regularly incorporating new data and updating the model. This allows the learning unit to always provide highly accurate diagnostic models based on the latest medical knowledge and data, strengthening diagnostic support in clinical settings.

[0034] The data collection unit can collect multiple types of medical image data. For example, the data collection unit can collect multiple types of medical image data, such as X-ray images, MRI images, and CT images. For example, the data collection unit can take X-ray images and save them as digital data. The data collection unit can also acquire MRI and CT images and save them as digital data. Furthermore, the data collection unit can automate the collection of medical image data using AI. For example, the data collection unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. This broadens the range of AI training data by collecting a diverse range of medical image data. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data.

[0035] The analysis unit can detect abnormalities in medical images using image recognition technology. For example, the analysis unit can detect abnormalities in medical images using image recognition technology. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. This allows for high-precision detection of abnormalities in medical images using image recognition technology. 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 use AI to analyze medical image data in real time and detect abnormalities.

[0036] The labeling unit can accurately label the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit can accurately label the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit can determine whether the lung nodule detected by the AI ​​is benign or malignant and assign an appropriate label. This improves the accuracy of labeling by leveraging the knowledge and experience of a specialized medical team. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can determine whether the lung nodule detected by the AI ​​is benign or malignant and assign an appropriate label.

[0037] The learning unit can improve diagnostic accuracy by learning based on labeled data. For example, the learning unit can use AI to learn based on labeled data and improve diagnostic accuracy. For instance, the learning unit can use AI to learn data on labeled lung nodules, enabling it to accurately determine whether nodules are benign or malignant when analyzing new X-ray images in the future. This improves the diagnostic accuracy of the AI ​​by learning based on labeled data. Some or all of the above processing in the learning unit may be performed using AI, or without AI. For example, the learning unit can use AI to learn based on labeled data and improve diagnostic accuracy.

[0038] The analysis unit can analyze medical images such as X-ray images, MRI images, and CT images. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can use AI to automate the analysis of medical image data. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. This improves the diagnostic capabilities of the AI ​​by analyzing various types of medical images. 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 use AI to analyze medical image data in real time and detect abnormalities.

[0039] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past collected data and adopt that method. The data collection unit can also analyze past collected data and find ways to reduce the time required for collection. Furthermore, the data collection unit can propose new methods to improve the accuracy of collection based on past collected data. In this way, the efficiency of the collection method is improved by analyzing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past collected data into AI and have the AI ​​select the optimal collection method.

[0040] The data collection unit can filter medical image data based on the patient's current medical history and symptoms. For example, the data collection unit can refer to the patient's medical history and prioritize the collection of medical image data related to specific symptoms. The data collection unit can also select necessary medical image data based on the patient's current symptoms. Furthermore, the data collection unit can combine the patient's medical history and current symptoms to collect the most relevant medical image data. This allows for the collection of more relevant data by filtering the data based on the patient's medical history and symptoms. 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 input the patient's medical history and current symptoms into the AI ​​and have the AI ​​perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information when collecting medical image data. For example, the data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information when collecting medical image data. For example, the data collection unit can collect data from the nearest medical facility based on the patient's geographical location information. The data collection unit can also prioritize the collection of data related to region-specific diseases, taking into account the patient's geographical location information. Furthermore, the data collection unit can collect data from easily accessible medical facilities based on the patient's geographical location information. This allows for the priority collection of data related to region-specific diseases by considering geographical location information. 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 input the patient's geographical location information into AI and have AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a patient's social media activity and collect relevant data when collecting medical image data. For example, the data collection unit can analyze a patient's social media activity and collect relevant data when collecting medical image data. For example, the data collection unit can analyze a patient's social media activity and collect relevant medical image data from health-related posts. The data collection unit can also prioritize the collection of data related to specific symptoms based on the patient's social media activity. Furthermore, the data collection unit can refer to the patient's social media activity, collect information about their health status, and associate it with medical image data. This allows for the collection of data related to the patient's health status by analyzing social media activity. 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 input the patient's social media activity into AI and have AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical images during the analysis. For example, the analysis unit can perform a detailed analysis on medical images of high importance. It can also perform a simplified analysis on medical images of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the medical images. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the medical images. 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 input the importance of the medical images into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the medical image during analysis. For example, the analysis unit can apply a specific algorithm to X-ray images to detect abnormalities. The analysis unit can also apply a different algorithm to MRI images to perform a more detailed analysis. Furthermore, the analysis unit can apply yet another algorithm to CT images to identify abnormalities. By applying analysis algorithms appropriate to the category of the medical image, the accuracy of the analysis is improved. 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 input the category of the medical image into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the time the medical images were taken. For example, the analysis unit may prioritize the analysis of the most recent medical images. It can also postpone the analysis of older medical images. Furthermore, the analysis unit can adjust the order of analysis based on the time the images were taken. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the time the images were taken. 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 input the time the medical images were taken into the AI ​​and have the AI ​​determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of medical images during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant medical images. It can also postpone the analysis of less relevant medical images. Furthermore, the analysis unit can determine the order of analysis based on the relevance of medical images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of medical images. 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 input the relevance of medical images into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0047] The labeling unit can adjust the level of detail of labels based on the reliability of the AI's analysis results during labeling. For example, the labeling unit can perform detailed labeling when the reliability of the AI's analysis results is high. Conversely, the labeling unit can perform concise labeling when the reliability of the AI's analysis results is low. Furthermore, the labeling unit can adjust the level of detail of labels according to the reliability of the AI's analysis results. This improves the accuracy of labeling by adjusting the level of detail of labels according to the reliability of the AI's analysis results. Some or all of the above processing in the labeling unit may be performed using AI, or not using AI. For example, the labeling unit can input the reliability of the AI's analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of labels.

[0048] The labeling unit can apply different labeling methods depending on the type of medical image during labeling. For example, the labeling unit can apply a specific labeling method to X-ray images. It can also apply a different labeling method to MRI images. Furthermore, it can apply yet another labeling method to CT images. This improves the accuracy of labeling by applying a labeling method appropriate to the type of medical image. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input the type of medical image into the AI ​​and have the AI ​​perform the application of the labeling method.

[0049] The labeling unit can adjust the label content based on the location where the medical image was taken during the labeling process. For example, if the image was taken at a specific hospital, the labeling unit will label the image based on the hospital's diagnostic criteria. The labeling unit can also perform labeling appropriate to different locations. Furthermore, the labeling unit can adjust the label content based on the location. This allows for more accurate labeling by adjusting the label content based on the location. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input location information into AI and have AI perform the adjustment of the label content.

[0050] The labeling unit can improve the accuracy of labels by referring to relevant literature on medical images during the labeling process. For example, the labeling unit can improve the accuracy of labels by referring to relevant literature on medical images during the labeling process. For example, the labeling unit can refer to literature related to medical images to perform accurate labeling. The labeling unit can also search for relevant literature based on the analysis results of medical images to improve the accuracy of labels. Furthermore, the labeling unit can perform detailed labeling by referring to relevant literature during the labeling process. This improves the accuracy of labeling by referring to relevant literature. Some or all of the above processes in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input relevant literature into AI and have AI perform the task of improving label accuracy.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This improves the accuracy of the learning algorithm by referring to past learning 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 input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0052] The learning unit can apply different learning methods to different types of medical images during training. For example, the learning unit can apply a specific learning method to X-ray images to detect abnormalities. It can also apply a different learning method to MRI images to perform detailed analysis. Furthermore, it can apply yet another learning method to CT images to identify abnormalities. By applying learning methods appropriate to the type of medical image, the accuracy of training is improved. 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 input the type of medical image into the AI ​​and have the AI ​​perform the application of the learning method.

[0053] The learning unit can weight the training data based on the timing of medical image acquisition during training. For example, the learning unit can weight the training data based on the timing of medical image acquisition during training. For example, the learning unit can assign a higher weight to the most recent medical image data. The learning unit can also assign a lower weight to older medical image data. Furthermore, the learning unit can adjust the weighting of the training data based on the acquisition timing. This makes it possible to perform training that emphasizes the most recent data by weighting the training data based on the acquisition timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the timing of medical image acquisition into AI and have AI perform the weighting of the training data.

[0054] The learning unit can improve the accuracy of its learning by referring to relevant market data for medical images during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to relevant market data for medical images during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to market data related to medical images. The learning unit can also improve the accuracy of its learning by searching for relevant market data based on the results of its medical image analysis. Furthermore, the learning unit can perform detailed learning by referring to relevant market data during the learning process for medical images. This improves the accuracy of the learning process by referring to relevant market data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input relevant market data into AI and have AI perform the improvement of the learning accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can supplement the analysis results of medical image data by referring to the patient's lifestyle data. For example, the analysis unit can refer to the patient's diet and exercise data to determine whether a particular abnormality is related to lifestyle. It can also consider the patient's smoking and drinking habits to assess the risk of abnormalities. Furthermore, the analysis unit can analyze the patient's sleep patterns to examine whether abnormalities are related to sleep deprivation. This allows for the provision of more comprehensive analysis results by referencing lifestyle data.

[0057] The data collection unit can determine the type of data to collect when collecting medical imaging data, taking into account the patient's genetic information. For example, based on the patient's genetic information, the data collection unit can prioritize the collection of relevant medical imaging data if there is a specific genetic risk. Furthermore, the data collection unit can also collect data that is useful for the early detection of specific diseases, based on genetic information. In addition, the data collection unit can predict future risks, taking genetic information into account, and collect corresponding data. This allows for more effective data collection by considering genetic information.

[0058] The labeling unit can adjust the label content by referring to the patient's treatment history based on the analysis results of the medical image during the labeling process. For example, the labeling unit can assign an appropriate label by considering the type and effect of treatments the patient has received in the past. Furthermore, the labeling unit can evaluate the impact of specific treatments on abnormalities based on the treatment history. In addition, the labeling unit can include information in the label that will be useful for future treatment planning by referring to the treatment history. This allows for more accurate labeling by considering the treatment history.

[0059] The learning unit can evaluate the reliability of the source of medical image data during training and prioritize learning from highly reliable data. For example, the learning unit can select highly reliable data based on the evaluation and track record of the medical institutions that collected the data. The learning unit can also evaluate the quality of the data collected from the source and prioritize learning from high-quality data. Furthermore, the learning unit can evaluate the data collection process of the source and select data where the process is appropriate. This improves the accuracy of the learning process by prioritizing highly reliable data.

[0060] The analysis unit can supplement the analysis results of medical image data by referring to the patient's environmental data. For example, the analysis unit can refer to data on the patient's living and working environment to determine whether a particular abnormality is related to environmental factors. The analysis unit can also consider air pollution and water quality data in the patient's residential area to assess the risk of abnormalities. Furthermore, the analysis unit can analyze changes in the patient's living environment to examine whether abnormalities are related to environmental changes. This allows for the provision of more comprehensive analysis results by referencing environmental data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects medical image data. Medical image data includes X-ray images, MRI images, CT images, etc. The collection unit captures these images and saves them as digital data. It can also use AI to analyze patient medical records and automatically collect necessary medical image data. Step 2: The analysis unit analyzes the data collected by the data acquisition unit. The analysis unit uses image recognition technology to detect abnormalities in medical images. For example, it can use AI to detect lung nodules in X-ray images or abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data and detect abnormalities in real time. Step 3: The labeling unit applies accurate labels based on the analysis results obtained by the analysis unit. The labeling unit applies accurate labels to the AI ​​analysis results based on the knowledge and experience of the expert medical team. For example, it determines whether the lung nodule detected by the AI ​​is benign or malignant and applies the appropriate label. Step 4: The learning unit learns based on the data labeled by the labeling unit. The learning unit uses AI to learn based on the labeled data and improve diagnostic accuracy. For example, by learning data on labeled lung nodules, it will be possible to determine with high accuracy whether a nodule is benign or malignant when analyzing new X-ray images in the future.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that establishes true image diagnosis by setting up a specialized medical team dedicated to labeling and classification work for AI, and proceeding with classification and data labeling. In this system, the specialized medical team collects medical image data, and the AI ​​analyzes that data. Next, the specialized medical team assigns accurate labels based on the AI's analysis results. This labeling work is used as training data for the AI, improving the AI's diagnostic accuracy. Ultimately, the AI ​​becomes able to perform highly accurate image diagnosis. For example, the specialized medical team collects medical image data. In this case, various types of medical images (e.g., X-ray images, MRI images, CT images, etc.) are collected. This allows the AI ​​to learn from diverse data. Next, the AI ​​analyzes the collected medical image data. The AI ​​uses image recognition technology to detect abnormalities in the images. For example, the AI ​​can analyze an X-ray image and detect a lung nodule. After that, the specialized medical team assigns accurate labels based on the AI's analysis results. This labeling work is performed based on the knowledge and experience of the specialized medical team. For example, the specialized medical team determines whether the nodule detected by the AI ​​is benign or malignant and assigns an appropriate label. Labeled data is used as training data for AI. The AI ​​learns from this labeled data to improve its diagnostic accuracy. For example, by learning from labeled nodule data, the AI ​​will be able to determine with high accuracy whether nodules are benign or malignant when analyzing new X-ray images in the future. Ultimately, the AI ​​will be able to perform highly accurate image diagnoses. This will improve diagnostic accuracy in medical settings and enable early detection and treatment of patients. For example, if the AI ​​can detect lung nodules with high accuracy and start treatment early, the patient's prognosis will improve. This allows the system to efficiently collect, analyze, label, and train on medical image data.

[0064] The image diagnostic system according to this embodiment comprises an acquisition unit, an analysis unit, a labeling unit, and a learning unit. The acquisition unit collects medical image data. Medical image data includes, but is not limited to, X-ray images, MRI images, and CT images. The acquisition unit, for example, takes X-ray images and stores them as digital data. The acquisition unit can also acquire MRI images and CT images and store them as digital data. Furthermore, the acquisition unit can automate the collection of medical image data using AI. For example, the acquisition unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. The analysis unit analyzes the data collected by the acquisition unit. The analysis unit can, for example, use image recognition technology to detect abnormalities in medical images. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI images and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. The labeling unit assigns accurate labels based on the analysis results obtained by the analysis unit. The labeling unit assigns accurate labels to the AI ​​analysis results based on, for example, the knowledge and experience of a specialized medical team. For example, the labeling unit determines whether the lung nodule detected by the AI ​​is benign or malignant and assigns an appropriate label. The learning unit performs learning based on the data labeled by the labeling unit. For example, the learning unit uses AI to learn from the labeled data and improve diagnostic accuracy. For example, the learning unit uses AI to learn from the data of labeled lung nodules, enabling it to determine with high accuracy whether nodules are benign or malignant when analyzing new X-ray images in the future. As a result, the image diagnostic system according to this embodiment can efficiently collect, analyze, label, and learn medical image data.

[0065] The data collection unit collects medical image data. This includes, but is not limited to, X-ray images, MRI images, and CT images. For example, the unit can take X-ray images and save them as digital data. It can also acquire MRI and CT images and save them as digital data. Furthermore, the data collection unit can automate the collection of medical image data using AI. For example, the unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. Specifically, the AI ​​analyzes the patient's electronic medical record and past medical records to identify necessary imaging tests based on specific symptoms and diagnoses. For example, if a lung abnormality is suspected, the AI ​​automatically instructs an X-ray and collects the image data. It can also link with MRI and CT appointment management systems to acquire images at the optimal time based on the patient's schedule. Furthermore, the data collection unit has a function to evaluate the quality of image data in real time and instruct re-shooting if inappropriate images are collected. This allows the data collection unit to efficiently collect high-quality medical image data and improve the accuracy of diagnosis.

[0066] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use image recognition technology to detect abnormalities in medical images. For instance, it can use AI to detect lung nodules in X-ray images. It can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, it can use AI to analyze medical image data in real time and detect abnormalities. Specifically, the AI ​​uses deep learning technology to learn from a vast dataset of medical images, improving the accuracy of abnormality detection. For example, to detect lung nodules, the AI ​​learns from thousands of X-ray images and recognizes the characteristics of the nodules with high accuracy. Similarly, in MRI and CT images, the AI ​​uses a multi-layer neural network to analyze and detect brain tumors and visceral abnormalities with high accuracy. In addition to detecting abnormalities, the analysis unit can also provide detailed information such as the type, location, and size of the abnormality. This allows physicians to make quick and accurate diagnoses and appropriately determine treatment plans for patients.

[0067] The labeling unit applies accurate labels based on the analysis results obtained by the analysis unit. For example, the labeling unit applies accurate labels to the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit determines whether a lung nodule detected by the AI ​​is benign or malignant and applies an appropriate label. Specifically, the labeling unit allows specialists to review the AI ​​analysis results and apply labels according to the type and severity of the abnormality. For example, if the lung nodule is benign, it will be labeled "benign nodule," and if it is malignant, it will be labeled "malignant nodule." The labeling unit can also add detailed information such as the location, size, and shape of the abnormality as part of the label. Furthermore, the labeling unit integrates the AI ​​analysis results and the specialist's judgment to build a feedback loop that improves the accuracy of the labels. This enables the labeling unit to achieve highly accurate labeling based on the AI ​​analysis results and improve the reliability of the diagnosis.

[0068] The learning unit learns based on data labeled by the labeling unit. For example, the learning unit uses AI to learn from the labeled data and improve diagnostic accuracy. For instance, the learning unit uses AI to learn from labeled lung nodule data, enabling it to accurately determine whether nodules are benign or malignant when analyzing new X-ray images in the future. Specifically, the learning unit iteratively learns from labeled medical image data using deep learning algorithms. For example, it uses a dataset of lung nodules to extract nodule features and build a model for determining benign / malignant nodules. Furthermore, the learning unit can integrate and learn from different types of medical image data (X-ray, MRI, CT, etc.) to increase the versatility of anomaly detection. In addition, the learning unit continuously improves diagnostic accuracy by regularly incorporating new data and updating the model. This allows the learning unit to always provide highly accurate diagnostic models based on the latest medical knowledge and data, strengthening diagnostic support in clinical settings.

[0069] The data collection unit can collect multiple types of medical image data. For example, the data collection unit can collect multiple types of medical image data, such as X-ray images, MRI images, and CT images. For example, the data collection unit can take X-ray images and save them as digital data. The data collection unit can also acquire MRI and CT images and save them as digital data. Furthermore, the data collection unit can automate the collection of medical image data using AI. For example, the data collection unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data. This broadens the range of AI training data by collecting a diverse range of medical image data. Some or all of the above-described processes in the data collection unit may be performed using AI, or they may not. For example, the data collection unit can use AI to analyze a patient's medical records and automatically collect the necessary medical image data.

[0070] The analysis unit can detect abnormalities in medical images using image recognition technology. For example, the analysis unit can detect abnormalities in medical images using image recognition technology. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data using AI. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. This allows for high-precision detection of abnormalities in medical images using image recognition technology. 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 use AI to analyze medical image data in real time and detect abnormalities.

[0071] The labeling unit can accurately label the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit can accurately label the AI ​​analysis results based on the knowledge and experience of a specialized medical team. For example, the labeling unit can determine whether the lung nodule detected by the AI ​​is benign or malignant and assign an appropriate label. This improves the accuracy of labeling by leveraging the knowledge and experience of a specialized medical team. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can determine whether the lung nodule detected by the AI ​​is benign or malignant and assign an appropriate label.

[0072] The learning unit can improve diagnostic accuracy by learning based on labeled data. For example, the learning unit can use AI to learn based on labeled data and improve diagnostic accuracy. For instance, the learning unit can use AI to learn data on labeled lung nodules, enabling it to accurately determine whether nodules are benign or malignant when analyzing new X-ray images in the future. This improves the diagnostic accuracy of the AI ​​by learning based on labeled data. Some or all of the above processing in the learning unit may be performed using AI, or without AI. For example, the learning unit can use AI to learn based on labeled data and improve diagnostic accuracy.

[0073] The analysis unit can analyze medical images such as X-ray images, MRI images, and CT images. For example, the analysis unit can use AI to detect lung nodules in X-ray images. The analysis unit can also detect abnormalities in MRI and CT images. Furthermore, the analysis unit can use AI to automate the analysis of medical image data. For example, the analysis unit can use AI to analyze medical image data in real time and detect abnormalities. This improves the diagnostic capabilities of the AI ​​by analyzing various types of medical images. 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 use AI to analyze medical image data in real time and detect abnormalities.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of medical image data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect medical image data according to the normal collection schedule. If the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can advance the collection timing to quickly collect medical image data. By adjusting the collection timing according to the user's emotions, medical image data can be collected at a more appropriate time. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0075] The data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can analyze past collected data and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past collected data and adopt that method. The data collection unit can also analyze past collected data and find ways to reduce the time required for collection. Furthermore, the data collection unit can propose new methods to improve the accuracy of collection based on past collected data. In this way, the efficiency of the collection method is improved by analyzing past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past collected data into AI and have the AI ​​select the optimal collection method.

[0076] The data collection unit can filter medical image data based on the patient's current medical history and symptoms. For example, the data collection unit can refer to the patient's medical history and prioritize the collection of medical image data related to specific symptoms. The data collection unit can also select necessary medical image data based on the patient's current symptoms. Furthermore, the data collection unit can combine the patient's medical history and current symptoms to collect the most relevant medical image data. This allows for the collection of more relevant data by filtering the data based on the patient's medical history and symptoms. 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 input the patient's medical history and current symptoms into the AI ​​and have the AI ​​perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of medical image data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect medical image data according to normal priorities. If the user is stressed, the data collection unit can also prioritize the collection of high-priority medical image data. Furthermore, if the user is in a hurry, the data collection unit can also prioritize the collection of medical image data that can be collected quickly. This allows for the priority collection of important data by prioritizing data 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information when collecting medical image data. For example, the data collection unit can prioritize the collection of highly relevant data based on the patient's geographical location information when collecting medical image data. For example, the data collection unit can collect data from the nearest medical facility based on the patient's geographical location information. The data collection unit can also prioritize the collection of data related to region-specific diseases, taking into account the patient's geographical location information. Furthermore, the data collection unit can collect data from easily accessible medical facilities based on the patient's geographical location information. This allows for the priority collection of data related to region-specific diseases by considering geographical location information. 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 input the patient's geographical location information into AI and have AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a patient's social media activity and collect relevant data when collecting medical image data. For example, the data collection unit can analyze a patient's social media activity and collect relevant data when collecting medical image data. For example, the data collection unit can analyze a patient's social media activity and collect relevant medical image data from health-related posts. The data collection unit can also prioritize the collection of data related to specific symptoms based on the patient's social media activity. Furthermore, the data collection unit can refer to the patient's social media activity, collect information about their health status, and associate it with medical image data. This allows for the collection of data related to the patient's health status by analyzing social media activity. 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 input the patient's social media activity into AI and have AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. It can also provide concise and to-the-point analysis results when the user is stressed. Furthermore, it can provide analysis results in a format that can be quickly understood when the user is in a hurry. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical images during the analysis. For example, the analysis unit can perform a detailed analysis on medical images of high importance. It can also perform a simplified analysis on medical images of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the medical images. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the medical images. 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 input the importance of the medical images into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the medical image during analysis. For example, the analysis unit can apply a specific algorithm to X-ray images to detect abnormalities. The analysis unit can also apply a different algorithm to MRI images to perform a more detailed analysis. Furthermore, the analysis unit can apply yet another algorithm to CT images to identify abnormalities. By applying analysis algorithms appropriate to the category of the medical image, the accuracy of the analysis is improved. 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 input the category of the medical image into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can perform a detailed analysis if the user is relaxed, or a concise analysis if the user is stressed. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The analysis unit can determine the priority of analysis based on the time the medical images were taken. For example, the analysis unit may prioritize the analysis of the most recent medical images. It can also postpone the analysis of older medical images. Furthermore, the analysis unit can adjust the order of analysis based on the time the images were taken. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the time the images were taken. 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 input the time the medical images were taken into the AI ​​and have the AI ​​determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of medical images during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant medical images. It can also postpone the analysis of less relevant medical images. Furthermore, the analysis unit can determine the order of analysis based on the relevance of medical images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of medical images. 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 input the relevance of medical images into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0086] The labeling unit can estimate the user's emotions and adjust the labeling method based on the estimated emotions. For example, the labeling unit can perform detailed labeling when the user is relaxed, or concise labeling when the user is stressed, or even faster labeling when the user is in a hurry. By adjusting the labeling method according to the user's emotions, more appropriate labeling becomes possible. Emotion estimation is achieved using an emotion estimation function, such as 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 labeling unit may be performed using AI or not. For example, the labeling unit can input user image data captured by a camera into the generative AI and have the generative AI perform the user's emotion estimation.

[0087] The labeling unit can adjust the level of detail of labels based on the reliability of the AI's analysis results during labeling. For example, the labeling unit can perform detailed labeling when the reliability of the AI's analysis results is high. Conversely, the labeling unit can perform concise labeling when the reliability of the AI's analysis results is low. Furthermore, the labeling unit can adjust the level of detail of labels according to the reliability of the AI's analysis results. This improves the accuracy of labeling by adjusting the level of detail of labels according to the reliability of the AI's analysis results. Some or all of the above processing in the labeling unit may be performed using AI, or not using AI. For example, the labeling unit can input the reliability of the AI's analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of labels.

[0088] The labeling unit can apply different labeling methods depending on the type of medical image during labeling. For example, the labeling unit can apply a specific labeling method to X-ray images. It can also apply a different labeling method to MRI images. Furthermore, it can apply yet another labeling method to CT images. This improves the accuracy of labeling by applying a labeling method appropriate to the type of medical image. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input the type of medical image into the AI ​​and have the AI ​​perform the application of the labeling method.

[0089] The labeling unit can estimate the user's emotions and determine labeling priorities based on the estimated emotions. For example, the labeling unit can label according to normal priorities when the user is relaxed. It can also prioritize high-importance labels when the user is stressed. Furthermore, it can label quickly when the user is in a hurry. This allows important labels to be prioritized by determining labeling priorities 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 labeling unit may be performed using AI or not. For example, the labeling unit can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0090] The labeling unit can adjust the label content based on the location where the medical image was taken during the labeling process. For example, if the image was taken at a specific hospital, the labeling unit will label the image based on the hospital's diagnostic criteria. The labeling unit can also perform labeling appropriate to different locations. Furthermore, the labeling unit can adjust the label content based on the location. This allows for more accurate labeling by adjusting the label content based on the location. Some or all of the above processing in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input location information into AI and have AI perform the adjustment of the label content.

[0091] The labeling unit can improve the accuracy of labels by referring to relevant literature on medical images during the labeling process. For example, the labeling unit can improve the accuracy of labels by referring to relevant literature on medical images during the labeling process. For example, the labeling unit can refer to literature related to medical images to perform accurate labeling. The labeling unit can also search for relevant literature based on the analysis results of medical images to improve the accuracy of labels. Furthermore, the labeling unit can perform detailed labeling by referring to relevant literature during the labeling process. This improves the accuracy of labeling by referring to relevant literature. Some or all of the above processes in the labeling unit may be performed using AI, for example, or without AI. For example, the labeling unit can input relevant literature into AI and have AI perform the task of improving label accuracy.

[0092] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can select detailed training data if the user is relaxed. It can also select concise training data if the user is stressed. Furthermore, if the user is in a hurry, it can select data that allows for quick learning. This enables more effective learning by selecting training data 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 input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This improves the accuracy of the learning algorithm by referring to past learning 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 input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0094] The learning unit can apply different learning methods to different types of medical images during training. For example, the learning unit can apply a specific learning method to X-ray images to detect abnormalities. It can also apply a different learning method to MRI images to perform detailed analysis. Furthermore, it can apply yet another learning method to CT images to identify abnormalities. By applying learning methods appropriate to the type of medical image, the accuracy of training is improved. 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 input the type of medical image into the AI ​​and have the AI ​​perform the application of the learning method.

[0095] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will learn at a normal learning frequency. If the user is stressed, the learning unit can reduce the learning frequency to lessen the user's burden. Furthermore, if the user is in a hurry, the learning unit can increase the learning frequency to learn more quickly. By adjusting the learning frequency according to the user's emotions, more effective learning 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0096] The learning unit can weight the training data based on the timing of medical image acquisition during training. For example, the learning unit can weight the training data based on the timing of medical image acquisition during training. For example, the learning unit can assign a higher weight to the most recent medical image data. The learning unit can also assign a lower weight to older medical image data. Furthermore, the learning unit can adjust the weighting of the training data based on the acquisition timing. This makes it possible to perform training that emphasizes the most recent data by weighting the training data based on the acquisition timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the timing of medical image acquisition into AI and have AI perform the weighting of the training data.

[0097] The learning unit can improve the accuracy of its learning by referring to relevant market data for medical images during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to relevant market data for medical images during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to market data related to medical images. The learning unit can also improve the accuracy of its learning by searching for relevant market data based on the results of its medical image analysis. Furthermore, the learning unit can perform detailed learning by referring to relevant market data during the learning process for medical images. This improves the accuracy of the learning process by referring to relevant market data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input relevant market data into AI and have AI perform the improvement of the learning accuracy.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can supplement the analysis results of medical image data by referring to the patient's lifestyle data. For example, the analysis unit can refer to the patient's diet and exercise data to determine whether a particular abnormality is related to lifestyle. It can also consider the patient's smoking and drinking habits to assess the risk of abnormalities. Furthermore, the analysis unit can analyze the patient's sleep patterns to examine whether abnormalities are related to sleep deprivation. This allows for the provision of more comprehensive analysis results by referencing lifestyle data.

[0100] The data collection unit can determine the type of data to collect when collecting medical imaging data, taking into account the patient's genetic information. For example, based on the patient's genetic information, the data collection unit can prioritize the collection of relevant medical imaging data if there is a specific genetic risk. Furthermore, the data collection unit can also collect data that is useful for the early detection of specific diseases, based on genetic information. In addition, the data collection unit can predict future risks, taking genetic information into account, and collect corresponding data. This allows for more effective data collection by considering genetic information.

[0101] The labeling unit can adjust the label content by referring to the patient's treatment history based on the analysis results of the medical image during the labeling process. For example, the labeling unit can assign an appropriate label by considering the type and effect of treatments the patient has received in the past. Furthermore, the labeling unit can evaluate the impact of specific treatments on abnormalities based on the treatment history. In addition, the labeling unit can include information in the label that will be useful for future treatment planning by referring to the treatment history. This allows for more accurate labeling by considering the treatment history.

[0102] The learning unit can evaluate the reliability of the source of medical image data during training and prioritize learning from highly reliable data. For example, the learning unit can select highly reliable data based on the evaluation and track record of the medical institutions that collected the data. The learning unit can also evaluate the quality of the data collected from the source and prioritize learning from high-quality data. Furthermore, the learning unit can evaluate the data collection process of the source and select data where the process is appropriate. This improves the accuracy of the learning process by prioritizing highly reliable data.

[0103] The analysis unit can supplement the analysis results of medical image data by referring to the patient's environmental data. For example, the analysis unit can refer to data on the patient's living and working environment to determine whether a particular abnormality is related to environmental factors. The analysis unit can also consider air pollution and water quality data in the patient's residential area to assess the risk of abnormalities. Furthermore, the analysis unit can analyze changes in the patient's living environment to examine whether abnormalities are related to environmental changes. This allows for the provision of more comprehensive analysis results by referencing environmental data.

[0104] The data collection unit can estimate the user's emotions and adjust the method of collecting medical image data based on those emotions. For example, if the user is relaxed, the unit will use the standard collection method. If the user is stressed, the unit can change the collection method to reduce the user's burden. Furthermore, if the user is in a hurry, the unit can select a method that allows for rapid collection. By adjusting the collection method according to the user's emotions, more appropriate data collection becomes possible.

[0105] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. In this way, by adjusting the presentation method of analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0106] The labeling unit can estimate the user's emotions and adjust the level of detail in the labeling based on those emotions. For example, if the user is relaxed, the labeling unit will provide detailed labeling. If the user is stressed, the labeling unit can provide concise labeling. Furthermore, if the user is in a hurry, the labeling unit can provide rapid labeling. By adjusting the level of detail in the labeling according to the user's emotions, more appropriate labeling becomes possible.

[0107] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is relaxed, the learning unit will select detailed training data. If the user is stressed, the learning unit can select concise training data. Furthermore, if the user is in a hurry, the learning unit can select data that allows for quick learning. By selecting training data according to the user's emotions, more effective learning becomes possible.

[0108] The data collection unit can estimate the user's emotions and determine the priority of medical image data to collect based on those emotions. For example, if the user is relaxed, the unit will collect medical image data according to normal priorities. If the user is stressed, the unit can also prioritize the collection of high-priority medical image data. Furthermore, if the user is in a hurry, the unit can prioritize the collection of medical image data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects medical image data. Medical image data includes X-ray images, MRI images, CT images, etc. The collection unit captures these images and saves them as digital data. It can also use AI to analyze patient medical records and automatically collect necessary medical image data. Step 2: The analysis unit analyzes the data collected by the data acquisition unit. The analysis unit uses image recognition technology to detect abnormalities in medical images. For example, it can use AI to detect lung nodules in X-ray images or abnormalities in MRI and CT images. Furthermore, the analysis unit can automate the analysis of medical image data and detect abnormalities in real time. Step 3: The labeling unit applies accurate labels based on the analysis results obtained by the analysis unit. The labeling unit applies accurate labels to the AI ​​analysis results based on the knowledge and experience of the expert medical team. For example, it determines whether the lung nodule detected by the AI ​​is benign or malignant and applies the appropriate label. Step 4: The learning unit learns based on the data labeled by the labeling unit. The learning unit uses AI to learn based on the labeled data and improve diagnostic accuracy. For example, by learning data on labeled lung nodules, it will be possible to determine with high accuracy whether a nodule is benign or malignant when analyzing new X-ray images in the future.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] For example, the data collection unit is implemented by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes medical image data. For example, the labeling unit is implemented by the specific processing unit 290 of the data processing device 12 and labels based on the knowledge and experience of a specialized medical team. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12 and the AI ​​learns based on the labeled data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, the data collection unit is implemented by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes medical image data. For example, the labeling unit is implemented by the specific processing unit 290 of the data processing device 12 and labels based on the knowledge and experience of a specialized medical team. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12 and the AI ​​learns based on the labeled data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] For example, the data collection unit is implemented by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes medical image data. For example, the labeling unit is implemented by the specific processing unit 290 of the data processing unit 12 and labels based on the knowledge and experience of a specialized medical team. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns based on the labeled data. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] For example, the data collection unit is implemented by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes medical image data. For example, the labeling unit is implemented by the specific processing unit 290 of the data processing unit 12 and labels based on the knowledge and experience of a specialized medical team. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns based on the labeled data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The collection unit collects medical image data, An analysis unit analyzes the data collected by the aforementioned collection unit, A labeling unit that assigns accurate labels based on the analysis results obtained by the analysis unit, The system includes a learning unit that performs learning based on the data labeled by the labeling unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect multiple types of medical image data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Detecting abnormalities in medical images using image recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 4) The labeling unit is Based on the knowledge and experience of a specialized medical team, the AI ​​analysis results are accurately labeled. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, Learn from labeled data to improve diagnostic accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze medical images such as X-ray, MRI, and CT scans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of medical image 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 Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting medical image data, filtering is performed based on the patient's current medical history and symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of medical image data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting medical imaging data, the system prioritizes the collection of highly relevant data based on the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting medical imaging data, analyze the patient's social media activity and collect relevant data. 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, During analysis, the level of detail is adjusted based on the importance of the medical images. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the medical image. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the medical images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the medical images. The system described in Appendix 1, characterized by the features described herein. (Note 19) The labeling unit is It estimates the user's emotions and adjusts the labeling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The labeling unit is When labeling, adjust the level of detail of the label based on the confidence level of the AI ​​analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The labeling unit is When labeling, different labeling methods are applied depending on the type of medical image. The system described in Appendix 1, characterized by the features described herein. (Note 22) The labeling unit is It estimates the user's emotions and determines the priority of labeling based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The labeling unit is When labeling, adjust the label content based on the location where the medical image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 24) The labeling unit is When labeling, we refer to relevant literature on medical images to improve labeling accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, different learning methods are applied to each type of medical image. The system described in Appendix 1, characterized by the features described herein. (Note 28) 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 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on when the medical images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, we improve the accuracy of the learning process by referencing relevant market data for medical images. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 medical image data, An analysis unit analyzes the data collected by the aforementioned collection unit, A labeling unit that assigns accurate labels based on the analysis results obtained by the analysis unit, The system includes a learning unit that performs learning based on the data labeled by the labeling unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect multiple types of medical image data. The system according to feature 1.

3. The aforementioned analysis unit, Detecting abnormalities in medical images using image recognition technology The system according to feature 1.

4. The labeling unit is Based on the knowledge and experience of a specialized medical team, the AI ​​analysis results are accurately labeled. The system according to feature 1.

5. The aforementioned learning unit, The system learns from the data labeled by the labeling unit and improves diagnostic accuracy. The system according to feature 1.

6. The aforementioned analysis unit, Analyze medical images such as X-ray, MRI, and CT scans. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of medical image data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting medical image data, filtering is performed based on the patient's current medical history and symptoms. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of medical image data to collect based on the estimated user emotions. The system according to feature 1.

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