System

Abstract

An object of the system according to the embodiment is to analyze medical AI using production data to efficiently provide diagnostic results and suggest a treatment plan.SOLUTION: The system includes a medical data analyzer, a diagnostic result provider, and a treatment plan suggester. The medical data analysis unit analyzes the patient medical data using the generated AI. The diagnosis result providing unit provides a diagnosis result based on the medical data analyzed by the medical data analysis unit. The treatment plan proposal unit proposes a treatment plan based on the diagnosis result provided by the diagnosis result providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not fully automated the analysis of medical data and the provision of diagnostic results, leaving room for improvement in the efficient proposition of medical diagnoses and treatment plans.

[0005] The system of the embodiment aims to analyze medical data using generative AI, efficiently provide diagnostic results, and propose treatment plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical data analysis unit, a diagnostic result providing unit, and a treatment plan proposing unit. The medical data analysis unit analyzes the patient's medical data using a generative AI. The diagnostic result providing unit provides a diagnostic result based on the medical data analyzed by the medical data analysis unit. The treatment plan proposing unit proposes a treatment plan based on the diagnostic result provided by the diagnostic result providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze medical data using generative AI, efficiently provide diagnostic results, and propose treatment plans. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A medical diagnosis and treatment system according to an embodiment of the present invention uses generative AI to analyze a patient's medical data and provide diagnostic results and treatment plans. This reduces the burden on medical professionals and improves diagnostic accuracy and treatment effectiveness.

[0029] A medical diagnosis and treatment system according to an embodiment includes a medical data analysis unit, a diagnostic result providing unit, and a treatment plan proposing unit. The medical data analysis unit analyzes a patient's medical data using a generative AI. For example, the medical data analysis unit analyzes the patient's electronic medical record to understand the patient's medical history and current health condition. The medical data analysis unit can also analyze the patient's image data to detect abnormalities. The medical data analysis unit can also analyze test results and identify abnormal values. For example, the medical data analysis unit analyzes text data in the electronic medical record using natural language processing technology to extract medical history and symptoms. The image data is analyzed using deep learning technology to detect abnormalities. The test results are analyzed using statistical methods to identify abnormal values. The diagnostic result providing unit provides a diagnostic result based on the medical data analyzed by the medical data analysis unit. For example, the diagnostic result providing unit outputs a diagnostic result indicating the possibility of a specific disease based on the data analyzed by the generative AI. The diagnostic result providing unit can also provide the diagnostic result in report format. The diagnostic result providing unit can also notify medical professionals of the diagnostic result. For example, the diagnostic result providing unit calculates a disease risk score based on the data analyzed by the generating AI and enters it in a report. The report is output in PDF format and stored in the electronic medical record system. The diagnostic results are notified to medical professionals via email or alert. The treatment plan proposing unit proposes a treatment plan based on the diagnostic results provided by the diagnostic result providing unit. For example, the treatment plan proposing unit proposes an optimal drug therapy based on the data analyzed by the generating AI. The treatment plan proposing unit can also determine whether surgery is appropriate. Furthermore, the treatment plan proposing unit can monitor the progress of treatment and modify the treatment plan as necessary. For example, the treatment plan proposing unit proposes the type and dosage of drug therapy based on the data analyzed by the generating AI. The patient's medical history and current health condition are taken into consideration when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as necessary. As a result, the medical diagnosis and treatment system according to the embodiment can reduce the burden on medical professionals and improve diagnostic accuracy and treatment effectiveness.For example, medical professionals can provide prompt and accurate medical care by referring to the diagnosis results and treatment plans provided by generative AI. Patients can also receive optimal treatment based on the treatment plans provided by generative AI. Furthermore, sales of the system are expected to increase the profits of medical institutions.

[0030] The medical data analysis unit can analyze a patient's MRI images and identify the presence and location of a tumor based on the MRI images. For example, the medical data analysis unit can analyze a patient's MRI images using deep learning technology to identify the presence and location of a tumor. The medical data analysis unit can also use image processing technology to evaluate the size and shape of a tumor. Furthermore, the medical data analysis unit can compare multiple MRI images to monitor the progression of a tumor. For example, the medical data analysis unit can use a deep learning model to extract tumor features from MRI images and identify the presence and location of a tumor. Image processing technology can be used to measure the size and shape of a tumor and record the results in a report. By comparing multiple MRI images, the progression of a tumor can be evaluated and the effectiveness of treatment can be monitored. This makes it possible to detect tumors early by analyzing MRI images.

[0031] The medical data analysis unit can analyze the patient's blood test results and detect abnormal values ​​based on the blood test results. For example, the medical data analysis unit can analyze the patient's blood test results using statistical methods to detect abnormal values. The medical data analysis unit can also identify patterns of abnormal values ​​using machine learning algorithms. Furthermore, the medical data analysis unit can provide the abnormal value detection results in report format. For example, the medical data analysis unit compares each item in the blood test results with the normal range to identify abnormal values. The machine learning algorithm is used to analyze the patterns of abnormal values ​​and identify the cause of the abnormality. The abnormal value detection results are written in a report and provided to medical professionals. This makes it possible to detect abnormal values ​​early by analyzing the blood test results.

[0032] The diagnostic result providing unit can comprehensively analyze the patient's symptoms and test results and output diagnostic results indicating the possibility of a specific disease. For example, the generation AI comprehensively analyzes the patient's symptoms and test results and outputs diagnostic results indicating the possibility of a specific disease. The diagnostic result providing unit can also provide diagnostic results in report format. Furthermore, the diagnostic result providing unit can notify medical professionals of the diagnostic results. For example, the diagnostic result providing unit calculates a disease risk score based on the data analyzed by the generation AI and includes it in a report. The report is output in PDF format and saved in the electronic medical record system. The diagnostic results are notified to medical professionals by email or alert. This makes it possible to provide diagnostic results indicating the possibility of a specific disease by comprehensively analyzing symptoms and test results.

[0033] The treatment plan proposal unit can determine whether drug therapy or surgery is appropriate, taking into account the patient's medical history and current health condition. For example, the generative AI analyzes the patient's medical history and current health condition to propose the most appropriate drug therapy. The treatment plan proposal unit can also determine whether surgery is appropriate. Furthermore, the treatment plan proposal unit can monitor the progress of treatment and modify the treatment plan as necessary. For example, the treatment plan proposal unit proposes the type and dosage of drug therapy based on the data analyzed by the generative AI. The patient's medical history and current health condition are taken into account when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as necessary. This makes it possible to propose the most appropriate treatment plan by taking into account the patient's medical history and health condition.

[0034] The treatment plan proposal unit can monitor the progress of treatment and revise the treatment plan as necessary. For example, the generative AI monitors the progress of treatment and revise the treatment plan as necessary. The treatment plan proposal unit can also evaluate the effectiveness of treatment based on periodic test results and patient reports. Furthermore, the treatment plan proposal unit can provide the results of treatment plan revisions in report format. For example, the treatment plan proposal unit proposes treatment plan revisions based on data analyzed by the generative AI. The effectiveness of treatment is evaluated based on periodic test results and patient reports, and revise the treatment plan as necessary. The results of treatment plan revisions are recorded in a report and provided to medical professionals. This allows the treatment progress to be monitored and the treatment plan to be revised in a timely manner.

[0035] The medical data analysis unit can analyze a patient's lifestyle data and perform a comprehensive health assessment based on the lifestyle data. For example, the generation AI in the medical data analysis unit can analyze a patient's lifestyle data and identify nutritional imbalances. The medical data analysis unit can also analyze exercise data and evaluate the risk of insufficient or excessive exercise. Furthermore, the medical data analysis unit can analyze sleep patterns and identify the risk of sleep disorders. For example, the generation AI in the medical data analysis unit can evaluate nutrient deficiencies or excesses based on dietary content and calorie intake. When analyzing exercise data, it can suggest an appropriate exercise plan based on the frequency and intensity of exercise. When analyzing sleep patterns, it can provide advice on improving sleep based on sleep duration and quality. This makes it possible to analyze lifestyle data more comprehensively to assess health.

[0036] The medical data analysis unit can take into account a patient's genetic information and identify genetic risk factors based on the genetic information. For example, the generation AI in the medical data analysis unit analyzes a patient's genetic information and evaluates the impact of specific gene mutations on disease risk. The medical data analysis unit can also evaluate genetic risk based on family history. Furthermore, the medical data analysis unit can perform an integrated analysis of genetic information and environmental factors to perform a comprehensive risk assessment. For example, the generation AI in the medical data analysis unit analyzes the impact of BRCA1 / 2 gene mutations on breast cancer risk. Based on family history, the unit evaluates diabetes risk and proposes preventive measures. An integrated analysis of genetic information and environmental factors is then performed to propose an individualized health management plan. This makes it possible to identify genetic risk factors by taking genetic information into account.

[0037] The medical data analysis unit can analyze environmental data and evaluate the impact of environmental factors on health based on the environmental data. For example, the generation AI can analyze air quality data in a residential area to evaluate the risk of respiratory disease. The medical data analysis unit can also analyze water quality data to evaluate the impact of water pollution on health. Furthermore, the medical data analysis unit can perform an integrated analysis of environmental data and health data to evaluate the combined impact of environmental factors on health. For example, the generation AI can identify asthma risk based on PM2.5 and pollen concentrations. It can analyze lead and toxic substance concentrations based on water quality data to identify kidney disease risk. It can perform an integrated analysis of environmental data and health data to evaluate the relationship between air quality and allergic symptoms and propose preventive measures. In this way, the impact of environmental factors on health can be evaluated by analyzing environmental data.

[0038] The medical data analysis unit can analyze a patient's socioeconomic background and evaluate the impact of social factors on health based on socioeconomic background. For example, the generative AI analyzes a patient's income data to evaluate the impact of economic stress on health. The medical data analysis unit can also analyze a patient's education level to evaluate the impact of health literacy on health behavior. Furthermore, the medical data analysis unit can perform an integrated analysis of socioeconomic background and health data to evaluate the combined impact of social factors on health. For example, the generative AI can identify the impact of low income on the risk of chronic disease. It can also analyze the impact of education level on the rate of preventive medical care consultations. It can also perform an integrated analysis of socioeconomic background and health data to evaluate the relationship between income and dietary habits and provide advice for health improvement. In this way, the impact of social factors on health can be evaluated by analyzing socioeconomic background.

[0039] The diagnostic result providing unit compares with past diagnostic data and can improve the reliability of the diagnosis based on the past diagnostic data. In the diagnostic result providing unit, for example, the generation AI analyzes past diagnostic data and compares it with the current diagnostic result. The diagnostic result providing unit can also analyze fluctuations in diagnostic results based on the past diagnostic data. Furthermore, the diagnostic result providing unit can perform an integrated analysis of past diagnostic data to improve the reliability of the diagnostic result. For example, in the diagnostic result providing unit, the generation AI compares past MRI images with current images to evaluate the progression of a tumor. It compares past blood test results with current results to evaluate fluctuations in abnormal values. It compares past diagnostic results with current symptoms to improve the accuracy of the diagnosis. In this way, by comparing with past diagnostic data, the reliability of the diagnosis can be improved.

[0040] The diagnostic result providing unit can take the patient's family history into consideration and assess genetic risk based on the family history. For example, the generating AI in the diagnostic result providing unit analyzes the patient's family history and assesses genetic risk. The diagnostic result providing unit can also evaluate genetic risk by integrating family history and current health data to assess genetic risk. Furthermore, the diagnostic result providing unit can suggest preventive measures against genetic risk based on family history. For example, the generating AI in the diagnostic result providing unit identifies the risk of heart disease if there are many family members with heart disease. It identifies the risk of diabetes based on family history and blood test results. It recommends regular checkups if there are many cancer patients based on family history. This makes it possible to assess genetic risk by taking family history into consideration.

[0041] The diagnostic result providing unit can share diagnostic results between different medical institutions and facilitate second opinions based on the diagnostic results. For example, the generation AI can integrate the diagnostic results into electronic medical records and share them between different medical institutions. The diagnostic result providing unit can also standardize the diagnostic results and facilitate sharing them between different medical institutions. Furthermore, the diagnostic result providing unit can provide a platform for sharing diagnostic results and facilitate second opinions. For example, the generation AI can store the diagnostic results on the cloud so that other medical institutions can access them. The diagnostic results can be stored in HL7 format so that they can be read by other medical institutions. A dedicated web portal for sharing diagnostic results can be provided to facilitate second opinions. This makes it possible to facilitate second opinions by sharing diagnostic results.

[0042] The diagnostic result providing unit provides diagnostic results on a portal site that can be accessed by patients themselves, and can promote self-management based on the diagnostic results. For example, the diagnostic result providing unit may have the generating AI provide diagnostic results on a portal site dedicated to patients, allowing patients to access their own diagnostic results. The diagnostic result providing unit may also notify patients of the diagnostic results and promote self-management. Furthermore, the diagnostic result providing unit may provide health management advice based on the diagnostic results to support patients' self-management. For example, the diagnostic result providing unit may have the generating AI upload diagnostic results to a web portal, allowing patients to access them. When the diagnostic results are updated, the diagnostic result providing unit notifies patients by email or SMS. Advice on improving lifestyle habits based on the diagnostic results is provided to support patients' self-management. In this way, providing diagnostic results on a portal site can promote patients' self-management.

[0043] The treatment plan proposal unit can achieve comprehensive health improvement based on the lifestyle improvement plan, including the patient's lifestyle improvement plan. In the treatment plan proposal unit, for example, the generation AI analyzes the patient's lifestyle data and proposes a lifestyle improvement plan. The treatment plan proposal unit can also integrate the lifestyle improvement plan into the treatment plan to achieve comprehensive health improvement. Furthermore, the treatment plan proposal unit can monitor the progress of the lifestyle improvement plan and modify the plan as necessary. For example, the treatment plan proposal unit provides a diet and exercise improvement plan using the generation AI. It proposes a plan that combines drug therapy and exercise therapy into the treatment plan. It monitors the progress of the lifestyle improvement plan and adjusts the frequency and intensity of exercise. In this way, by including the lifestyle improvement plan, comprehensive health improvement can be achieved.

[0044] The treatment plan proposal unit can share treatment plans between different medical institutions and ensure consistency of treatment based on the treatment plan. For example, the generation AI can integrate the treatment plan into an electronic medical record and share it between different medical institutions. The treatment plan proposal unit can also standardize the treatment plan to facilitate sharing between different medical institutions. Furthermore, the treatment plan proposal unit can provide a platform for sharing treatment plans and ensure consistency of treatment. For example, the generation AI can store the treatment plan on the cloud so that other medical institutions can access it. The treatment plan proposal unit can store the treatment plan in HL7 format so that it can be read by other medical institutions. A dedicated web portal for sharing treatment plans can be provided to ensure consistency of treatment. This makes it possible to ensure consistency of treatment by sharing treatment plans.

[0045] The treatment plan proposal unit can provide the treatment plan on a portal site that the patient can access themselves, and promote self-management based on the treatment plan. In the treatment plan proposal unit, for example, the generation AI provides the treatment plan on a patient-specific portal site, allowing the patient to access their own treatment plan. The treatment plan proposal unit can also notify the patient of the treatment plan and promote self-management. Furthermore, the treatment plan proposal unit can provide health management advice based on the treatment plan to support patient self-management. For example, in the treatment plan proposal unit, the generation AI uploads the treatment plan to a web portal, allowing the patient to access it. When the treatment plan is updated, the treatment plan proposal unit notifies the patient by email or SMS. Advice on improving lifestyle habits based on the treatment plan is provided to support patient self-management. In this way, by providing the treatment plan on a portal site, patient self-management can be promoted.

[0046] The system can be used as an educational tool for medical professionals to improve their diagnostic and treatment skills. For example, a system using generative AI can be used as an educational tool for medical professionals to improve their diagnostic skills. The system can also perform treatment simulations based on proposed treatment plans to improve treatment skills. Furthermore, the system can be used for continuing education for medical professionals to provide them with the latest medical knowledge. For example, the system can practice diagnosis through simulations using generative AI. It can perform treatment simulations based on proposed treatment plans. It can provide educational content based on the latest research results and guidelines. As a result, by using the system as an educational tool for medical professionals, it can improve their diagnostic and treatment skills.

[0047] The system can be applied to telemedicine to eliminate regional disparities in medical care based on telemedicine. For example, the system can apply a system using generative AI to telemedicine to eliminate regional disparities in medical care. The system can also provide diagnostic results and treatment plans to patients in remote locations. Furthermore, the system can collaborate with remote medical institutions to share diagnostic results. For example, the system can apply a system using generative AI to telemedicine to provide diagnostic results and treatment plans to patients in remote locations. Collaborate with remote medical institutions to share diagnostic results. Provide educational content to medical professionals in remote locations to improve their diagnostic and treatment skills. In this way, applying the system to telemedicine can eliminate regional disparities in medical care.

[0048] The system can also be applied to other medical departments, broadening the scope of medical treatment based on other medical departments. For example, a system using generative AI can be applied to dentistry to support dental treatment. The system can also be applied to ophthalmology to support ophthalmology treatment. The system can also be applied to other medical departments to broaden the scope of medical treatment. For example, a system using generative AI can be applied to dentistry to analyze dental images and support the diagnosis of cavities and periodontal disease. A system using generative AI can be applied to ophthalmology to analyze fundus images and support the diagnosis of glaucoma and retinal diseases. A system using generative AI can be applied to other medical departments to support medical treatment in dermatology and otolaryngology. This allows the system to be applied to other medical departments to broaden the scope of medical treatment.

[0049] The system can also be applied to rehabilitation and preventive medicine, allowing for the provision of comprehensive medical services based on rehabilitation and preventive medicine. For example, the system can apply a system using generative AI to rehabilitation to propose rehabilitation plans. The system can also apply a system using generative AI to preventive medicine to propose health management plans. Furthermore, the system can apply a system using generative AI to comprehensive medical services to support patient health management. For example, the system can apply a system using generative AI to rehabilitation to analyze exercise data and provide an optimal rehabilitation plan. The system can apply a system using generative AI to preventive medicine to analyze lifestyle habit data and propose preventive measures. The system can be applied to comprehensive medical services to provide support at each stage of diagnosis, treatment, rehabilitation, and prevention. This makes it possible to provide comprehensive medical services by applying the system to rehabilitation and preventive medicine.

[0050] When selling a system, it is possible to strengthen the post-implementation support system and support the medical institution's operations. When selling a system, for example, after selling a system that uses generative AI, an implementation support team can be dispatched to support the medical institution's operations. When selling a system, it is also possible to set up a 24-hour support desk to support the medical institution's operations. Furthermore, when selling a system, it is also possible to provide regular maintenance and updates to support the medical institution's operations. For example, when selling a system, the implementation support team provides initial system setup and training. The 24-hour support desk provides system troubleshooting and technical support. Regular maintenance and updates allow for system upgrades and security measures. In this way, it is possible to strengthen the post-implementation support system and support the medical institution's operations.

[0051] When selling a system, customization options can be offered, allowing the system to be built to meet the needs of a medical institution. When selling a system, for example, when selling a system that uses generative AI, customization options can be offered to meet the needs of a medical institution. When selling a system, it is also possible to customize the interface to meet the requests of the medical institution. Furthermore, when selling a system, it is also possible to customize the data analysis functions to meet the needs of the medical institution. For example, when selling a system, functions specialized for a specific medical department can be added. Integration with electronic medical record systems can be strengthened at the request of the medical institution. Analysis functions for specific test data can be added. In this way, by offering customization options, it is possible to build a system that meets the needs of a medical institution.

[0052] When selling a system, it is possible to offer a joint purchase plan between different medical institutions and reduce costs based on the joint purchase plan. When selling a system, for example, when selling a system using generative AI, it is possible to offer a plan for multiple medical institutions to purchase jointly and reduce costs. When selling a system, it is also possible to promote joint use between medical institutions and reduce costs. Furthermore, when selling a system, it is possible to share the implementation costs through a joint purchase plan. For example, when selling a system, a discount can be applied for joint purchases. A cloud-based system can be provided so that it can be shared by multiple medical institutions. The initial investment can be shared among multiple medical institutions, reducing the cost burden. In this way, by offering a joint purchase plan, it is possible to reduce costs.

[0053] When selling a system, a leasing option can be offered, and the initial investment can be reduced based on the leasing option. When selling a system, for example, when selling a system using generative AI, a leasing option can be offered to reduce the initial investment of medical institutions. When selling a system, it is also possible to support the medical institution's cash flow through a leasing contract. Furthermore, using a leasing option when selling a system can make it easier for medical institutions to introduce the latest technology. For example, when selling a system, the system can be made available for a monthly fee. A plan can be offered that includes maintenance and support during the lease period. An option to upgrade to a new system after the lease period has expired can be offered. In this way, offering a leasing option can reduce the initial investment.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Medical diagnosis and treatment systems can also collect lifestyle data on patients and provide health management advice. For example, the system can analyze a patient's diet and exercise data and suggest improvements to nutritional balance and exercise volume. The system can also analyze sleep data and provide advice on improving sleep quality. Furthermore, the system can monitor stress levels and suggest relaxation methods for stress management. This allows patients to receive specific advice on maintaining their health in their daily lives.

[0056] Medical diagnosis and treatment systems can also assess health risks by taking into account a patient's socioeconomic background. For example, the system can analyze a patient's income and education level to assess the impact of economic stress on health. The system can also analyze a patient's living environment data to assess the impact of the living environment on health. Furthermore, the system can consider the presence or absence of a social support network to assess the impact of feelings of isolation on health. This allows for a comprehensive assessment of a patient's socioeconomic background and a more accurate understanding of health risks.

[0057] Medical diagnostic and treatment systems can also analyze patients' genetic information and provide personalized preventive medical plans. For example, the system can assess the impact of specific gene mutations on disease risk and suggest preventive measures. The system can also assess genetic risk based on family history and recommend regular checkups. Furthermore, the system can perform an integrated analysis of genetic information and environmental factors to perform a comprehensive risk assessment. This allows patients to receive preventive medical plans based on their genetic risk.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The medical data analysis unit uses generative AI to analyze the patient's medical data. For example, it analyzes the patient's electronic medical record to understand their medical history and current health condition. It can also analyze image data to detect abnormalities. It can also analyze test results to identify outliers. Specifically, it analyzes the text data in the electronic medical record using natural language processing technology to extract medical history and symptoms. Deep learning technology is used to detect abnormalities in the analysis of image data, and statistical methods are used to identify outliers in the analysis of test results. Step 2: The diagnostic result provider provides diagnostic results based on the medical data analyzed by the medical data analyzer. For example, based on the data analyzed by the generative AI, it outputs diagnostic results indicating the possibility of a specific disease. The diagnostic results can also be provided in report format and notified to medical professionals. Specifically, a disease risk score is calculated and included in the report. The report is output in PDF format and saved in the electronic medical record system. Medical professionals are notified of the diagnostic results via email or alert. Step 3: The treatment plan proposal unit proposes a treatment plan based on the diagnosis results provided by the diagnosis result provision unit. For example, it proposes the optimal drug therapy based on the data analyzed by the generative AI. It can also determine whether surgery is appropriate, monitor the progress of treatment, and modify the treatment plan as needed. Specifically, it proposes the type and dosage of drug therapy, and takes into account the patient's medical history and current health condition when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as needed.

[0060] (Example 2) A medical diagnosis and treatment system according to an embodiment of the present invention uses generative AI to analyze a patient's medical data and provide diagnostic results and treatment plans. This reduces the burden on medical professionals and improves diagnostic accuracy and treatment effectiveness.

[0061] A medical diagnosis and treatment system according to an embodiment includes a medical data analysis unit, a diagnostic result providing unit, and a treatment plan proposing unit. The medical data analysis unit analyzes a patient's medical data using a generative AI. For example, the medical data analysis unit analyzes the patient's electronic medical record to understand the patient's medical history and current health condition. The medical data analysis unit can also analyze the patient's image data to detect abnormalities. The medical data analysis unit can also analyze test results and identify abnormal values. For example, the medical data analysis unit analyzes text data in the electronic medical record using natural language processing technology to extract medical history and symptoms. The image data is analyzed using deep learning technology to detect abnormalities. The test results are analyzed using statistical methods to identify abnormal values. The diagnostic result providing unit provides a diagnostic result based on the medical data analyzed by the medical data analysis unit. For example, the diagnostic result providing unit outputs a diagnostic result indicating the possibility of a specific disease based on the data analyzed by the generative AI. The diagnostic result providing unit can also provide the diagnostic result in report format. The diagnostic result providing unit can also notify medical professionals of the diagnostic result. For example, the diagnostic result providing unit calculates a disease risk score based on the data analyzed by the generating AI and enters it in a report. The report is output in PDF format and stored in the electronic medical record system. The diagnostic results are notified to medical professionals via email or alert. The treatment plan proposing unit proposes a treatment plan based on the diagnostic results provided by the diagnostic result providing unit. For example, the treatment plan proposing unit proposes an optimal drug therapy based on the data analyzed by the generating AI. The treatment plan proposing unit can also determine whether surgery is appropriate. Furthermore, the treatment plan proposing unit can monitor the progress of treatment and modify the treatment plan as necessary. For example, the treatment plan proposing unit proposes the type and dosage of drug therapy based on the data analyzed by the generating AI. The patient's medical history and current health condition are taken into consideration when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as necessary. As a result, the medical diagnosis and treatment system according to the embodiment can reduce the burden on medical professionals and improve diagnostic accuracy and treatment effectiveness.For example, medical professionals can provide prompt and accurate medical care by referring to the diagnosis results and treatment plans provided by generative AI. Patients can also receive optimal treatment based on the treatment plans provided by generative AI. Furthermore, sales of the system are expected to increase the profits of medical institutions.

[0062] The medical data analysis unit can analyze a patient's MRI images and identify the presence and location of a tumor based on the MRI images. For example, the medical data analysis unit can analyze a patient's MRI images using deep learning technology to identify the presence and location of a tumor. The medical data analysis unit can also use image processing technology to evaluate the size and shape of a tumor. Furthermore, the medical data analysis unit can compare multiple MRI images to monitor the progression of a tumor. For example, the medical data analysis unit can use a deep learning model to extract tumor features from MRI images and identify the presence and location of a tumor. Image processing technology can be used to measure the size and shape of a tumor and record the results in a report. By comparing multiple MRI images, the progression of a tumor can be evaluated and the effectiveness of treatment can be monitored. This makes it possible to detect tumors early by analyzing MRI images.

[0063] The medical data analysis unit can analyze the patient's blood test results and detect abnormal values ​​based on the blood test results. For example, the medical data analysis unit can analyze the patient's blood test results using statistical methods to detect abnormal values. The medical data analysis unit can also identify patterns of abnormal values ​​using machine learning algorithms. Furthermore, the medical data analysis unit can provide the abnormal value detection results in report format. For example, the medical data analysis unit compares each item in the blood test results with the normal range to identify abnormal values. The machine learning algorithm is used to analyze the patterns of abnormal values ​​and identify the cause of the abnormality. The abnormal value detection results are written in a report and provided to medical professionals. This makes it possible to detect abnormal values ​​early by analyzing the blood test results.

[0064] The diagnostic result providing unit can comprehensively analyze the patient's symptoms and test results and output diagnostic results indicating the possibility of a specific disease. For example, the generation AI comprehensively analyzes the patient's symptoms and test results and outputs diagnostic results indicating the possibility of a specific disease. The diagnostic result providing unit can also provide diagnostic results in report format. Furthermore, the diagnostic result providing unit can notify medical professionals of the diagnostic results. For example, the diagnostic result providing unit calculates a disease risk score based on the data analyzed by the generation AI and includes it in a report. The report is output in PDF format and saved in the electronic medical record system. The diagnostic results are notified to medical professionals by email or alert. This makes it possible to provide diagnostic results indicating the possibility of a specific disease by comprehensively analyzing symptoms and test results.

[0065] The treatment plan proposal unit can determine whether drug therapy or surgery is appropriate, taking into account the patient's medical history and current health condition. For example, the generative AI analyzes the patient's medical history and current health condition to propose the most appropriate drug therapy. The treatment plan proposal unit can also determine whether surgery is appropriate. Furthermore, the treatment plan proposal unit can monitor the progress of treatment and modify the treatment plan as necessary. For example, the treatment plan proposal unit proposes the type and dosage of drug therapy based on the data analyzed by the generative AI. The patient's medical history and current health condition are taken into account when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as necessary. This makes it possible to propose the most appropriate treatment plan by taking into account the patient's medical history and health condition.

[0066] The treatment plan proposal unit can monitor the progress of treatment and revise the treatment plan as necessary. For example, the generative AI monitors the progress of treatment and revise the treatment plan as necessary. The treatment plan proposal unit can also evaluate the effectiveness of treatment based on periodic test results and patient reports. Furthermore, the treatment plan proposal unit can provide the results of treatment plan revisions in report format. For example, the treatment plan proposal unit proposes treatment plan revisions based on data analyzed by the generative AI. The effectiveness of treatment is evaluated based on periodic test results and patient reports, and revise the treatment plan as necessary. The results of treatment plan revisions are recorded in a report and provided to medical professionals. This allows the treatment progress to be monitored and the treatment plan to be revised in a timely manner.

[0067] The medical data analysis unit can analyze a patient's lifestyle data and perform a comprehensive health assessment based on the lifestyle data. For example, the generation AI in the medical data analysis unit can analyze a patient's lifestyle data and identify nutritional imbalances. The medical data analysis unit can also analyze exercise data and evaluate the risk of insufficient or excessive exercise. Furthermore, the medical data analysis unit can analyze sleep patterns and identify the risk of sleep disorders. For example, the generation AI in the medical data analysis unit can evaluate nutrient deficiencies or excesses based on dietary content and calorie intake. When analyzing exercise data, it can suggest an appropriate exercise plan based on the frequency and intensity of exercise. When analyzing sleep patterns, it can provide advice on improving sleep based on sleep duration and quality. This makes it possible to analyze lifestyle data more comprehensively to assess health.

[0068] The medical data analysis unit can take into account a patient's genetic information and identify genetic risk factors based on the genetic information. For example, the generation AI in the medical data analysis unit analyzes a patient's genetic information and evaluates the impact of specific gene mutations on disease risk. The medical data analysis unit can also evaluate genetic risk based on family history. Furthermore, the medical data analysis unit can perform an integrated analysis of genetic information and environmental factors to perform a comprehensive risk assessment. For example, the generation AI in the medical data analysis unit analyzes the impact of BRCA1 / 2 gene mutations on breast cancer risk. Based on family history, the unit evaluates diabetes risk and proposes preventive measures. An integrated analysis of genetic information and environmental factors is then performed to propose an individualized health management plan. This makes it possible to identify genetic risk factors by taking genetic information into account.

[0069] The medical data analysis unit can analyze environmental data and evaluate the impact of environmental factors on health based on the environmental data. For example, the generation AI can analyze air quality data in a residential area to evaluate the risk of respiratory disease. The medical data analysis unit can also analyze water quality data to evaluate the impact of water pollution on health. Furthermore, the medical data analysis unit can perform an integrated analysis of environmental data and health data to evaluate the combined impact of environmental factors on health. For example, the generation AI can identify asthma risk based on PM2.5 and pollen concentrations. It can analyze lead and toxic substance concentrations based on water quality data to identify kidney disease risk. It can perform an integrated analysis of environmental data and health data to evaluate the relationship between air quality and allergic symptoms and propose preventive measures. In this way, the impact of environmental factors on health can be evaluated by analyzing environmental data.

[0070] The medical data analysis unit can analyze a patient's socioeconomic background and evaluate the impact of social factors on health based on socioeconomic background. For example, the generative AI analyzes a patient's income data to evaluate the impact of economic stress on health. The medical data analysis unit can also analyze a patient's education level to evaluate the impact of health literacy on health behavior. Furthermore, the medical data analysis unit can perform an integrated analysis of socioeconomic background and health data to evaluate the combined impact of social factors on health. For example, the generative AI can identify the impact of low income on the risk of chronic disease. It can also analyze the impact of education level on the rate of preventive medical care consultations. It can also perform an integrated analysis of socioeconomic background and health data to evaluate the relationship between income and dietary habits and provide advice for health improvement. In this way, the impact of social factors on health can be evaluated by analyzing socioeconomic background.

[0071] The medical data analysis unit uses the emotion estimation function to monitor a patient's emotional state in real time and evaluate the impact of emotional fluctuations on health based on fluctuations in the emotional state. In the medical data analysis unit, for example, the generation AI monitors a patient's emotional state in real time and tracks emotional fluctuations. The medical data analysis unit can also perform an integrated analysis of emotional data and health data to evaluate the impact of emotional fluctuations on health. Furthermore, the medical data analysis unit can predict the impact of emotional fluctuations on health based on the emotional data. For example, in the medical data analysis unit, the generation AI analyzes emotional fluctuations based on data from a smartwatch. The integrated analysis of emotional data and health data evaluates the relationship between emotional fluctuations and blood pressure and provides stress management advice. The emotional data can be used to analyze the impact of emotional fluctuations on heart disease risk and suggest preventive measures. In this way, the impact of emotional fluctuations on health can be evaluated by monitoring emotional states in real time.

[0072] The diagnostic result providing unit compares with past diagnostic data and can improve the reliability of the diagnosis based on the past diagnostic data. In the diagnostic result providing unit, for example, the generation AI analyzes past diagnostic data and compares it with the current diagnostic result. The diagnostic result providing unit can also analyze fluctuations in diagnostic results based on the past diagnostic data. Furthermore, the diagnostic result providing unit can perform an integrated analysis of past diagnostic data to improve the reliability of the diagnostic result. For example, in the diagnostic result providing unit, the generation AI compares past MRI images with current images to evaluate the progression of a tumor. It compares past blood test results with current results to evaluate fluctuations in abnormal values. It compares past diagnostic results with current symptoms to improve the accuracy of the diagnosis. In this way, by comparing with past diagnostic data, the reliability of the diagnosis can be improved.

[0073] The diagnostic result providing unit can take the patient's family history into consideration and assess genetic risk based on the family history. For example, the generating AI in the diagnostic result providing unit analyzes the patient's family history and assesses genetic risk. The diagnostic result providing unit can also evaluate genetic risk by integrating family history and current health data to assess genetic risk. Furthermore, the diagnostic result providing unit can suggest preventive measures against genetic risk based on family history. For example, the generating AI in the diagnostic result providing unit identifies the risk of heart disease if there are many family members with heart disease. It identifies the risk of diabetes based on family history and blood test results. It recommends regular checkups if there are many cancer patients based on family history. This makes it possible to assess genetic risk by taking family history into consideration.

[0074] The diagnostic result providing unit can use the emotion estimation function to evaluate the emotional impact of the diagnostic result on the patient and suggest appropriate counseling based on the emotional impact. For example, when the generation AI provides a diagnostic result, the diagnostic result providing unit analyzes the patient's emotional state and evaluates the emotional impact. The diagnostic result providing unit can also predict the emotional impact of the diagnostic result on the patient based on the emotion data. Furthermore, the diagnostic result providing unit can suggest appropriate counseling based on the emotion data. For example, the diagnostic result providing unit analyzes the facial expression and voice when the generation AI hears the diagnostic result to evaluate the emotional impact. Based on the emotion data, it evaluates the stress caused by a diagnosis result of a serious disease. Based on the emotion data, it suggests counseling to reduce anxiety about the diagnostic result. In this way, it is possible to evaluate the emotional impact of the diagnostic result on the patient and suggest appropriate counseling.

[0075] The diagnostic result providing unit can share diagnostic results between different medical institutions and facilitate second opinions based on the diagnostic results. For example, the generation AI can integrate the diagnostic results into electronic medical records and share them between different medical institutions. The diagnostic result providing unit can also standardize the diagnostic results and facilitate sharing them between different medical institutions. Furthermore, the diagnostic result providing unit can provide a platform for sharing diagnostic results and facilitate second opinions. For example, the generation AI can store the diagnostic results on the cloud so that other medical institutions can access them. The diagnostic results can be stored in HL7 format so that they can be read by other medical institutions. A dedicated web portal for sharing diagnostic results can be provided to facilitate second opinions. This makes it possible to facilitate second opinions by sharing diagnostic results.

[0076] The diagnostic result providing unit provides diagnostic results on a portal site that can be accessed by patients themselves, and can promote self-management based on the diagnostic results. For example, the diagnostic result providing unit may have the generating AI provide diagnostic results on a portal site dedicated to patients, allowing patients to access their own diagnostic results. The diagnostic result providing unit may also notify patients of the diagnostic results and promote self-management. Furthermore, the diagnostic result providing unit may provide health management advice based on the diagnostic results to support patients' self-management. For example, the diagnostic result providing unit may have the generating AI upload diagnostic results to a web portal, allowing patients to access them. When the diagnostic results are updated, the diagnostic result providing unit notifies patients by email or SMS. Advice on improving lifestyle habits based on the diagnostic results is provided to support patients' self-management. In this way, providing diagnostic results on a portal site can promote patients' self-management.

[0077] The diagnostic result providing unit can use the emotion estimation function to monitor the emotional impact of the diagnostic result on the patient in real time and provide psychological support as needed based on the emotional impact. For example, the diagnostic result providing unit monitors the patient's emotional state in real time when the generation AI provides the diagnostic result. The diagnostic result providing unit can also evaluate the emotional impact of the diagnostic result on the patient in real time based on the emotion data. The diagnostic result providing unit can also provide psychological support as needed based on the emotion data. For example, the diagnostic result providing unit analyzes the facial expressions and voice of the generation AI when it hears the diagnostic result to evaluate the emotional impact. The diagnostic result providing unit analyzes the stress level regarding the diagnostic result based on the emotion data. The diagnostic result providing unit provides counseling in real time to reduce anxiety regarding the diagnostic result based on the emotion data. This makes it possible to monitor the emotional impact of the diagnostic result on the patient in real time and provide psychological support as needed.

[0078] The treatment plan proposal unit can achieve comprehensive health improvement based on the lifestyle improvement plan, including the patient's lifestyle improvement plan. In the treatment plan proposal unit, for example, the generation AI analyzes the patient's lifestyle data and proposes a lifestyle improvement plan. The treatment plan proposal unit can also integrate the lifestyle improvement plan into the treatment plan to achieve comprehensive health improvement. Furthermore, the treatment plan proposal unit can monitor the progress of the lifestyle improvement plan and modify the plan as necessary. For example, the treatment plan proposal unit provides a diet and exercise improvement plan using the generation AI. It proposes a plan that combines drug therapy and exercise therapy into the treatment plan. It monitors the progress of the lifestyle improvement plan and adjusts the frequency and intensity of exercise. In this way, by including the lifestyle improvement plan, comprehensive health improvement can be achieved.

[0079] The treatment plan proposal unit can use the emotion estimation function to evaluate the emotional impact of the treatment plan on the patient and improve treatment acceptability based on the emotional impact. For example, when the generation AI provides a treatment plan, the treatment plan proposal unit analyzes the patient's emotional state and evaluates the emotional impact. The treatment plan proposal unit can also predict the emotional impact of the treatment plan on the patient based on the emotion data. Furthermore, the treatment plan proposal unit can provide advice to improve treatment plan acceptability based on the emotion data. For example, the treatment plan proposal unit analyzes the facial expressions and voice of the generation AI when it hears the treatment plan to evaluate the emotional impact. Based on the emotion data, it evaluates anxiety and stress regarding the treatment plan. Based on the emotion data, it suggests counseling to reduce anxiety regarding the treatment plan. This makes it possible to evaluate the emotional impact of the treatment plan on the patient and improve treatment acceptability.

[0080] The treatment plan proposal unit can share treatment plans between different medical institutions and ensure consistency of treatment based on the treatment plan. For example, the generation AI can integrate the treatment plan into an electronic medical record and share it between different medical institutions. The treatment plan proposal unit can also standardize the treatment plan to facilitate sharing between different medical institutions. Furthermore, the treatment plan proposal unit can provide a platform for sharing treatment plans and ensure consistency of treatment. For example, the generation AI can store the treatment plan on the cloud so that other medical institutions can access it. The treatment plan proposal unit can store the treatment plan in HL7 format so that it can be read by other medical institutions. A dedicated web portal for sharing treatment plans can be provided to ensure consistency of treatment. This makes it possible to ensure consistency of treatment by sharing treatment plans.

[0081] The treatment plan proposal unit can provide the treatment plan on a portal site that the patient can access themselves, and promote self-management based on the treatment plan. In the treatment plan proposal unit, for example, the generation AI provides the treatment plan on a patient-specific portal site, allowing the patient to access their own treatment plan. The treatment plan proposal unit can also notify the patient of the treatment plan and promote self-management. Furthermore, the treatment plan proposal unit can provide health management advice based on the treatment plan to support patient self-management. For example, in the treatment plan proposal unit, the generation AI uploads the treatment plan to a web portal, allowing the patient to access it. When the treatment plan is updated, the treatment plan proposal unit notifies the patient by email or SMS. Advice on improving lifestyle habits based on the treatment plan is provided to support patient self-management. In this way, by providing the treatment plan on a portal site, patient self-management can be promoted.

[0082] The treatment plan proposal unit can use the emotion estimation function to monitor the emotional impact of the treatment plan on the patient in real time and provide psychological support as needed based on the emotional impact. For example, the treatment plan proposal unit monitors the patient's emotional state in real time when the generation AI provides a treatment plan. The treatment plan proposal unit can also evaluate the emotional impact of the treatment plan on the patient in real time based on the emotion data. The treatment plan proposal unit can also provide psychological support as needed based on the emotion data. For example, the treatment plan proposal unit analyzes the facial expressions and voice of the generation AI when it hears the treatment plan to evaluate the emotional impact. Based on the emotion data, it analyzes the stress level regarding the treatment plan. Based on the emotion data, it provides counseling in real time to reduce anxiety regarding the treatment plan. This makes it possible to monitor the emotional impact of the treatment plan on the patient in real time and provide psychological support as needed.

[0083] The system can be used as an educational tool for medical professionals to improve their diagnostic and treatment skills. For example, a system using generative AI can be used as an educational tool for medical professionals to improve their diagnostic skills. The system can also perform treatment simulations based on proposed treatment plans to improve treatment skills. Furthermore, the system can be used for continuing education for medical professionals to provide them with the latest medical knowledge. For example, the system can practice diagnosis through simulations using generative AI. It can perform treatment simulations based on proposed treatment plans. It can provide educational content based on the latest research results and guidelines. As a result, by using the system as an educational tool for medical professionals, it can improve their diagnostic and treatment skills.

[0084] The system can be applied to telemedicine to eliminate regional disparities in medical care based on telemedicine. For example, the system can apply a system using generative AI to telemedicine to eliminate regional disparities in medical care. The system can also provide diagnostic results and treatment plans to patients in remote locations. Furthermore, the system can collaborate with remote medical institutions to share diagnostic results. For example, the system can apply a system using generative AI to telemedicine to provide diagnostic results and treatment plans to patients in remote locations. Collaborate with remote medical institutions to share diagnostic results. Provide educational content to medical professionals in remote locations to improve their diagnostic and treatment skills. In this way, applying the system to telemedicine can eliminate regional disparities in medical care.

[0085] The system uses the emotion estimation function to monitor the stress levels of medical workers and improve the working environment based on the stress levels. For example, the system uses a generative AI to monitor the emotional state of medical workers in real time and evaluate their stress levels. The system can also predict the stress levels of medical workers based on the emotion data and suggest measures to improve the working environment. Furthermore, the system can support stress management for medical workers based on the emotion data. For example, the generative AI analyzes facial expressions and voice to identify the causes of stress. Based on the emotion data, the system suggests measures to address the causes of stress. Based on the emotion data, the system suggests counseling or relaxation programs to reduce stress. In this way, the working environment can be improved by monitoring the stress levels of medical workers.

[0086] The system can also be applied to other medical departments, broadening the scope of medical treatment based on other medical departments. For example, a system using generative AI can be applied to dentistry to support dental treatment. The system can also be applied to ophthalmology to support ophthalmology treatment. The system can also be applied to other medical departments to broaden the scope of medical treatment. For example, a system using generative AI can be applied to dentistry to analyze dental images and support the diagnosis of cavities and periodontal disease. A system using generative AI can be applied to ophthalmology to analyze fundus images and support the diagnosis of glaucoma and retinal diseases. A system using generative AI can be applied to other medical departments to support medical treatment in dermatology and otolaryngology. This allows the system to be applied to other medical departments to broaden the scope of medical treatment.

[0087] The system can also be applied to rehabilitation and preventive medicine, allowing for the provision of comprehensive medical services based on rehabilitation and preventive medicine. For example, the system can apply a system using generative AI to rehabilitation to propose rehabilitation plans. The system can also apply a system using generative AI to preventive medicine to propose health management plans. Furthermore, the system can apply a system using generative AI to comprehensive medical services to support patient health management. For example, the system can apply a system using generative AI to rehabilitation to analyze exercise data and provide an optimal rehabilitation plan. The system can apply a system using generative AI to preventive medicine to analyze lifestyle habit data and propose preventive measures. The system can be applied to comprehensive medical services to provide support at each stage of diagnosis, treatment, rehabilitation, and prevention. This makes it possible to provide comprehensive medical services by applying the system to rehabilitation and preventive medicine.

[0088] When selling a system, it is possible to strengthen the post-implementation support system and support the medical institution's operations. When selling a system, for example, after selling a system that uses generative AI, an implementation support team can be dispatched to support the medical institution's operations. When selling a system, it is also possible to set up a 24-hour support desk to support the medical institution's operations. Furthermore, when selling a system, it is also possible to provide regular maintenance and updates to support the medical institution's operations. For example, when selling a system, the implementation support team provides initial system setup and training. The 24-hour support desk provides system troubleshooting and technical support. Regular maintenance and updates allow for system upgrades and security measures. In this way, it is possible to strengthen the post-implementation support system and support the medical institution's operations.

[0089] When selling a system, customization options can be offered, allowing the system to be built to meet the needs of a medical institution. When selling a system, for example, when selling a system that uses generative AI, customization options can be offered to meet the needs of a medical institution. When selling a system, it is also possible to customize the interface to meet the requests of the medical institution. Furthermore, when selling a system, it is also possible to customize the data analysis functions to meet the needs of the medical institution. For example, when selling a system, functions specialized for a specific medical department can be added. Integration with electronic medical record systems can be strengthened at the request of the medical institution. Analysis functions for specific test data can be added. In this way, by offering customization options, it is possible to build a system that meets the needs of a medical institution.

[0090] When selling a system, the emotion estimation function can be used to evaluate the satisfaction of medical professionals after the system is introduced, collect feedback based on that satisfaction, and improve the system based on that feedback. When selling a system, for example, the generation AI can monitor the emotional state of medical professionals in real time and evaluate their satisfaction after the system is introduced. When selling a system, the emotion data can also be used to predict the satisfaction of medical professionals after the system is introduced and collect feedback. Furthermore, when selling a system, the emotion data can also be used to suggest improvements to the system. For example, when selling a system, the generation AI can analyze facial expressions and voices while using the system to evaluate satisfaction. Based on the emotion data, it can identify areas of dissatisfaction with the system and areas for improvement. Based on the emotion data, it can improve the interface and add functions to reduce stress for medical professionals. This makes it possible to evaluate the satisfaction of medical professionals after the system is introduced and improve the system based on that feedback.

[0091] When selling a system, it is possible to offer a joint purchase plan between different medical institutions and reduce costs based on the joint purchase plan. When selling a system, for example, when selling a system using generative AI, it is possible to offer a plan for multiple medical institutions to purchase jointly and reduce costs. When selling a system, it is also possible to promote joint use between medical institutions and reduce costs. Furthermore, when selling a system, it is possible to share the implementation costs through a joint purchase plan. For example, when selling a system, a discount can be applied for joint purchases. A cloud-based system can be provided so that it can be shared by multiple medical institutions. The initial investment can be shared among multiple medical institutions, reducing the cost burden. In this way, by offering a joint purchase plan, it is possible to reduce costs.

[0092] When selling a system, a leasing option can be offered, and the initial investment can be reduced based on the leasing option. When selling a system, for example, when selling a system using generative AI, a leasing option can be offered to reduce the initial investment of medical institutions. When selling a system, it is also possible to support the medical institution's cash flow through a leasing contract. Furthermore, using a leasing option when selling a system can make it easier for medical institutions to introduce the latest technology. For example, when selling a system, the system can be made available for a monthly fee. A plan can be offered that includes maintenance and support during the lease period. An option to upgrade to a new system after the lease period has expired can be offered. In this way, offering a leasing option can reduce the initial investment.

[0093] When selling a system, the emotion estimation function can be used to monitor the satisfaction of medical professionals in real time after the system is introduced, and support can be provided as needed based on their satisfaction. When selling a system, for example, the generation AI can monitor the emotional state of medical professionals in real time and evaluate their satisfaction after the system is introduced. When selling a system, the emotion data can also be used to evaluate the satisfaction of medical professionals in real time after the system is introduced, and support can be provided as needed. When selling a system, the emotion data can also be used to continuously monitor the satisfaction of medical professionals after the system is introduced, and support can be provided as needed. For example, when selling a system, the generation AI can analyze facial expressions and voices while using the system to evaluate satisfaction. Based on the emotion data, it can identify and address any dissatisfaction with the system and areas for improvement. Based on the emotion data, it can collect regular feedback and propose improvements to the system. This makes it possible to monitor the satisfaction of medical professionals in real time after the system is introduced, and provide support as needed.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] Medical diagnosis and treatment systems can also collect lifestyle data on patients and provide health management advice. For example, the system can analyze a patient's diet and exercise data and suggest improvements to nutritional balance and exercise volume. The system can also analyze sleep data and provide advice on improving sleep quality. Furthermore, the system can monitor stress levels and suggest relaxation methods for stress management. This allows patients to receive specific advice on maintaining their health in their daily lives.

[0096] Medical diagnosis and treatment systems can also assess health risks by taking into account a patient's socioeconomic background. For example, the system can analyze a patient's income and education level to assess the impact of economic stress on health. The system can also analyze a patient's living environment data to assess the impact of the living environment on health. Furthermore, the system can consider the presence or absence of a social support network to assess the impact of feelings of isolation on health. This allows for a comprehensive assessment of a patient's socioeconomic background and a more accurate understanding of health risks.

[0097] Medical diagnostic and treatment systems can also analyze patients' genetic information and provide personalized preventive medical plans. For example, the system can assess the impact of specific gene mutations on disease risk and suggest preventive measures. The system can also assess genetic risk based on family history and recommend regular checkups. Furthermore, the system can perform an integrated analysis of genetic information and environmental factors to perform a comprehensive risk assessment. This allows patients to receive preventive medical plans based on their genetic risk.

[0098] The medical diagnosis and treatment system can also monitor a patient's emotional state and evaluate the impact of emotional fluctuations on health. For example, the system can analyze emotional fluctuations based on data from a smartwatch and evaluate stress levels. The system can also integrate and analyze emotional data and health data to evaluate the impact of emotional fluctuations on blood pressure and heart rate. Furthermore, the system can provide stress management advice based on emotional fluctuations. This allows patients to understand their emotional state in real time and use it to manage their health.

[0099] The medical diagnosis and treatment system can also take into account the patient's emotional state and adjust the way in which the diagnosis results are presented. For example, the system can analyze the patient's emotional state when providing the diagnosis results and notify the results at an appropriate time. The system can also adjust the way in which the diagnosis results are explained to the patient based on the emotional data to help the patient understand. Furthermore, the system can suggest counseling based on the emotional data to reduce anxiety about the diagnosis results. This makes it easier for the patient to accept the diagnosis results and receive appropriate treatment.

[0100] The medical diagnosis and treatment system can also take into account the patient's emotional state and adjust how it proposes a treatment plan. For example, when proposing a treatment plan, the system can analyze the patient's emotional state and notify them of the plan at the appropriate time. The system can also adjust how it explains the treatment plan based on emotional data to promote patient understanding. Furthermore, the system can also suggest counseling to reduce anxiety about the treatment plan based on emotional data. This makes it easier for patients to accept the treatment plan and maximizes the effectiveness of treatment.

[0101] The medical diagnosis and treatment system can also monitor the progress of treatment by taking into account the patient's emotional state. For example, the system can analyze the patient's emotional state when monitoring the progress of treatment and evaluate the effectiveness of the treatment. The system can also provide advice based on the emotional data according to the progress of treatment. Furthermore, the system can suggest counseling to reduce anxiety about the progress of treatment based on the emotional data. This allows patients to understand the progress of treatment in real time and maximize the effectiveness of treatment.

[0102] The medical diagnosis and treatment system can also take the patient's emotional state into account when revising the treatment plan. For example, the system can analyze the patient's emotional state when revising the treatment plan and notify them of the revised plan at the appropriate time. The system can also adjust the way it explains the revised plan based on the emotional data to promote patient understanding. Furthermore, the system can suggest counseling based on the emotional data to reduce anxiety about the revised plan. This makes it easier for the patient to accept the revised plan and maximizes the effectiveness of treatment.

[0103] The medical diagnosis and treatment system can also monitor the progress of treatment by taking into account the patient's emotional state. For example, the system can analyze the patient's emotional state when monitoring the progress of treatment and evaluate the effectiveness of the treatment. The system can also provide advice based on the emotional data according to the progress of treatment. Furthermore, the system can suggest counseling to reduce anxiety about the progress of treatment based on the emotional data. This allows patients to understand the progress of treatment in real time and maximize the effectiveness of treatment.

[0104] The medical diagnosis and treatment system can also take the patient's emotional state into account when revising the treatment plan. For example, the system can analyze the patient's emotional state when revising the treatment plan and notify them of the revised plan at the appropriate time. The system can also adjust the way it explains the revised plan based on the emotional data to promote patient understanding. Furthermore, the system can suggest counseling based on the emotional data to reduce anxiety about the revised plan. This makes it easier for the patient to accept the revised plan and maximizes the effectiveness of treatment.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The medical data analysis unit uses generative AI to analyze the patient's medical data. For example, it analyzes the patient's electronic medical record to understand their medical history and current health condition. It can also analyze image data to detect abnormalities. It can also analyze test results to identify outliers. Specifically, it analyzes the text data in the electronic medical record using natural language processing technology to extract medical history and symptoms. Deep learning technology is used to detect abnormalities in the analysis of image data, and statistical methods are used to identify outliers in the analysis of test results. Step 2: The diagnostic result provider provides diagnostic results based on the medical data analyzed by the medical data analyzer. For example, based on the data analyzed by the generative AI, it outputs diagnostic results indicating the possibility of a specific disease. The diagnostic results can also be provided in report format and notified to medical professionals. Specifically, a disease risk score is calculated and included in the report. The report is output in PDF format and saved in the electronic medical record system. Medical professionals are notified of the diagnostic results via email or alert. Step 3: The treatment plan proposal unit proposes a treatment plan based on the diagnosis results provided by the diagnosis result provision unit. For example, it proposes the optimal drug therapy based on the data analyzed by the generative AI. It can also determine whether surgery is appropriate, monitor the progress of treatment, and modify the treatment plan as needed. Specifically, it proposes the type and dosage of drug therapy, and takes into account the patient's medical history and current health condition when determining whether surgery is appropriate. The progress of treatment is monitored based on regular test results and patient reports, and the treatment plan is modified as needed.

[0107] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A medical data analysis unit that uses generative AI to analyze patient medical data; a diagnostic result providing unit that provides a diagnostic result based on the medical data analyzed by the medical data analyzing unit; a treatment plan proposal unit that proposes a treatment plan based on the diagnostic results provided by the diagnostic result providing unit. A system characterized by:

2. The medical data analysis unit Analyzing a patient's MRI images and identifying the presence and location of a tumor based on the MRI images 2. The system of claim 1.

3. The medical data analysis unit Analyzing the patient's blood test results and detecting abnormal values ​​based on the blood test results 2. The system of claim 1.

4. The diagnostic result providing unit Comprehensively analyzes the patient's symptoms and test results, and outputs a diagnosis indicating the possibility of a specific disease.

2. The system of claim 1.

5. The treatment plan proposal unit Consider the patient's medical history and current health condition to determine whether drug therapy or surgery is appropriate 2. The system of claim 1.

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