Medical assistance and information service system based on artificial intelligence

Abstract

The invention relates to a medical assistance and information service system based on artificial intelligence, and discloses a medical assistance and information service system based on artificial intelligence. The system comprises eight core modules: a camera scanning and uploading module, a patient information classification and storage module, an artificial intelligence interpretation analysis and suggestion module, a user interaction module, a medical information safety protection module, a multi-terminal adaptation module, a health reminding module and a medical resource docking module. The system uploads a paper detection report and a medical image through scanning, automatically classifies according to the name of a patient and establishes an exclusive file; basic analysis is carried out through a local private medical database and an AI model, optimization is carried out in combination with Internet public medical resources, and cross-mechanism cooperation is supported; a graphical interface is adopted to adapt to multiple terminals, and health reminding and medical resource docking services are provided. According to the system, the medical information processing efficiency can be effectively improved, the data security is guaranteed, and intelligent medical assistance and information services are provided for medical employees and patients.

Description

Technical Field

[0001] This invention relates to the cross-application of artificial intelligence technology and medical services, specifically, an AI-based medical assistance and information service system. By integrating modules such as camera scanning and uploading, intelligent patient information management, AI-powered deep analysis and interpretation, and multi-terminal interaction, this system achieves efficient processing of medical test reports and medical images. It provides diagnostic support for medical professionals and convenient medical information retrieval and health management services for patients. Applicable to medical institutions at all levels, community health service centers, and family health management scenarios, it aims to improve medical service efficiency, ensure medical data security, and promote the collaborative sharing of medical resources. Background Technology

[0002] In the current operation of the healthcare system, medical services face many pressing issues that urgently need to be addressed. These issues not only affect the work efficiency of medical professionals but also cause inconvenience to patients' medical experience and health management. Specifically, they manifest in the following aspects:

[0003] First, the efficiency of medical information collection and uploading is low. In traditional medical settings, the entry of information from paper-based test reports and medical imaging films mainly relies on manual input, which not only consumes a lot of time and manpower but is also prone to errors due to human negligence, thus affecting the accuracy of subsequent diagnostic results. At the same time, medical imaging films require specialized equipment for reading and transmission, and compatibility issues between different devices often lead to inefficient transmission of image information, delaying the diagnostic process.

[0004] Secondly, patient medical information management is disorganized. In medical institutions, patient medical information is often scattered across different systems or departments, lacking unified classification and integration. When patients seek follow-up visits or cross-departmental consultations, medical professionals struggle to quickly and comprehensively access the patient's past medical records, increasing the probability of duplicate examinations, wasting medical resources, and potentially leading to diagnostic errors due to incomplete information. Furthermore, patients themselves find it difficult to conveniently manage and access their personal medical information, hindering long-term monitoring of their health.

[0005] Furthermore, medical data analysis and recommendations lack intelligent support. Currently, medical practitioners rely primarily on their personal professional experience to analyze test reports and medical images. Limited by differences in knowledge and clinical experience, the analysis results may be inconsistent. Simultaneously, the medical field updates rapidly, with new diagnostic standards and treatment plans constantly emerging. Medical practitioners struggle to keep up with all the latest medical information in real time, leading to analysis conclusions that may not reflect the latest medical advancements. In addition, data sharing barriers exist in cross-institutional and cross-departmental medical collaborations, hindering the participation of multidisciplinary experts in diagnosis and affecting the accuracy of diagnosing complex diseases.

[0006] Finally, medical data security and privacy protection face challenges. With the advancement of medical informatization, medical data faces security risks such as leakage and tampering during transmission, storage, and use. Existing medical systems often lack adequate security measures, including robust data encryption and access control mechanisms, making it difficult to protect patients' medical data privacy and reducing patients' trust in medical informatization services.

[0007] To address the aforementioned issues, there is an urgent need in the field for a comprehensive medical assistance and information service system capable of efficiently collecting, intelligently managing, and deeply analyzing medical information while ensuring data security. Based on this, this invention proposes an artificial intelligence-based medical assistance and information service system. Through modular design and integration of multiple functions, it effectively improves the efficiency and quality of medical services while protecting patient data privacy. Summary of the Invention

[0008] Purpose of the Invention: This invention aims to provide an AI-based medical assistance and information service system. Through the collaborative work of multiple functional modules, it achieves efficient collection, intelligent classification and storage, in-depth AI interpretation and analysis, and convenient user interaction of medical information, while ensuring medical data security. It also supports multi-terminal adaptation, health reminders, and integration with medical resources, providing comprehensive medical assistance and information services for medical professionals and patients. The core advantages of this system are: adopting an AI analysis model that combines local and public medical resources, balancing data privacy protection with the comprehensiveness of analysis conclusions; supporting multi-angle, batch scanning and uploading to improve the efficiency of medical information collection; achieving automatic classification and rapid retrieval of patient information to optimize medical information management; and providing multi-terminal, personalized interaction methods to enhance the user experience.

[0009] Technical Solution: The core architecture of this system includes the following modules

[0010] 1. Camera scanning and uploading module

[0011] This module is the core entry point for medical information collection, designed to solve the problems of low efficiency and error-proneness in traditional manual data entry. Its specific functions are as follows:

[0012] Hardware configuration: Includes a flexibly positioned camera component, supporting installation on the top, bottom, or both of the device to adapt to different usage scenarios. The top camera can be a rotating camera or a combination of a camera and a prism, allowing for flexible adjustment of the shooting angle to ensure complete capture of various paper-based test reports (such as blood routine reports and biochemical test reports) and medical imaging films (such as X-ray films and CT scans). The bottom camera is suitable for flat-lay scanning scenarios, facilitating quick placement and scanning of batches of documents.

[0013] Technical Support: High-definition acquisition technology ensures the clarity of captured images, providing a high-quality image foundation for subsequent information recognition and analysis. It also features automatic image correction and sharpness optimization functions, automatically correcting image tilt and distortion caused by shooting angle deviations, and optimizing blurry images to ensure that uploaded images meet information recognition standards.

[0014] Operational efficiency: Supports batch recognition and upload function, allowing users to place multiple test reports or images at once. The system automatically completes continuous scanning and information upload, greatly reducing manual operation steps and improving the efficiency of medical information collection.

[0015] 2. Patient information classification and storage module

[0016] This module primarily enables automated and standardized management of patient medical information, addressing the issues of fragmented information and difficulty in retrieval. Specific functions include:

[0017] Automatic Categorization and File Creation: The system can automatically extract patient name information from uploaded medical information and categorize newly uploaded medical information (such as test report data and imaging data) according to the patient's name, supplementing it into the patient's existing personalized file. If the system does not have a personalized file for the patient, it will automatically create a file named after the patient and store the uploaded medical information in it, realizing "one file per patient" management of patient information.

[0018] Information backup and secure storage: It has a comprehensive information backup function, using a combination of local backup and cloud backup (users can choose the backup method themselves) to prevent medical information from being damaged due to equipment failure, data loss and other problems, and to ensure the security and integrity of patients' medical information.

[0019] Quick search function: Users can retrieve file information by searching for keywords. Searchable keywords include patient name, upload time, test type (such as "May 2024 blood routine report"), etc. After the user enters the keywords, the system can quickly match and display the corresponding files, which makes it convenient for medical practitioners to quickly obtain the patient's past medical records and improve diagnostic efficiency.

[0020] 3. Artificial Intelligence Interpretation, Analysis, and Recommendation Module

[0021] This module is the core analysis unit of the system. Through two sub-modules that can operate independently or collaboratively, it enables in-depth interpretation of medical information and generation of professional suggestions, balancing data privacy and comprehensive analysis. Specifically, it includes:

[0022] Private medical database and model module

[0023] Operating mode: The medical database and specialized AI model are deployed locally, supporting independent operation without relying on the Internet, effectively avoiding the risk of privacy leakage of medical data during transmission and ensuring data security.

[0024] Analysis Functions: Prioritizing the use of established local databases (such as standard blood count and imaging structure databases) and specialized AI models (such as lesion identification and abnormal indicator analysis models), the system performs multi-dimensional fundamental analysis on textual information (such as test item names and diagnostic conclusions), numerical information (such as white blood cell count and blood glucose levels), and image information from medical images in uploaded reports. Specifically, it can identify abnormal detection indicators (such as indicators with values ​​exceeding the normal range) and abnormal imaging areas (such as lesion locations), and preliminarily determine abnormal characteristics (such as lesion size and morphology) and potential health problems, generating basic interpretations that conform to medical standards to ensure the accuracy and security of the analysis results.

[0025] Public Medical Resources and Collaboration Module

[0026] Information Supplementation and Conclusion Optimization: Based on the basic analysis results generated by the private medical database and model modules, the latest medical information (such as the latest diagnostic guidelines and research results on testing indicators) is accessed through the internet to optimize and improve the basic analysis conclusions. For example, for blood test reports, specific reference standards for different populations (such as the elderly and pregnant women) can be supplemented to correct the judgment bias based on general standards in the basic analysis; for imaging reports, case data of rare lesions can be supplemented to help identify rare diseases.

[0027] Data sharing and collaboration features: Data sharing is enabled based on user needs (such as cross-team consultations and regional medical collaboration), supporting multi-party participation in the analysis process (such as doctors from different departments and experts from different medical institutions). For example, radiologists can upload image interpretation opinions, and clinicians can supplement diagnostic suggestions based on patient symptoms, achieving multidisciplinary collaborative diagnosis and generating more comprehensive medical recommendations that are in line with the latest medical advancements. These recommendations include further examination suggestions (e.g., "A chest CT scan is recommended to clarify the nature of the lung shadow"), lifestyle adjustment suggestions (e.g., "Control sugar intake and exercise for 30 minutes daily"), and recommendations for medical departments (e.g., "Visit the cardiology department").

[0028] Traceability: The entire analysis process and the basis for the recommendations are traceable. The system records the databases, model versions, internet information sources, and operation records of collaborators used in the analysis. Users can view the analysis logic at any time, enhancing their trust in the analysis results.

[0029] Differentiation analysis and optimization adjustment

[0030] Differential Analysis: Supports targeted analysis of different types of test reports. For blood test reports, the private module primarily utilizes local databases and analysis models for complete blood count, biochemical indicators, and immune indicators to interpret deviations from normal ranges and their clinical significance; the public module supplements the latest research findings and reference standards for special populations, and supports data sharing among physicians across departments. For imaging test reports, the private module primarily utilizes a standard database of imaging structures and lesion identification models to identify structural abnormalities and lesion characteristics; the public module supplements the latest diagnostic guidelines and rare case data, and supports collaborative editing and interpretation of reports by radiologists and clinical physicians, facilitating the connection between diagnosis and treatment recommendations.

[0031] Updates and optimizations: It has the ability to update data and models regularly or in real time. It can automatically or manually update the local database and AI model according to the knowledge updates in the medical field (such as new diagnostic criteria and newly discovered lesion characteristics). At the same time, it can adjust the module parameters according to user feedback (such as the correction opinions of medical practitioners on the analysis results) to adapt to the needs of different application scenarios (such as basic diagnosis in community hospitals and consultation of complex diseases in tertiary hospitals).

[0032] 4. User Interaction Module

[0033] This module aims to provide convenient and personalized operation and information access methods for different users, including medical practitioners and patients. Specific functions are as follows:

[0034] Interface Design: A graphical interface is adopted, with a simple and clear layout and intuitive operation process, reducing the learning cost for users. For example, the interface for medical practitioners highlights function buttons such as editing analysis results and inviting collaborations; while the patient interface focuses on displaying personal health reports, health suggestions, and other content.

[0035] Multi-terminal support: Supports login on multiple terminals, including computers, tablets, and mobile phones. Users can access the system through different devices and view medical information and perform operations anytime, anywhere.

[0036] Exclusive Functions and Display Methods: Dedicated functions are provided for different users. Medical professionals can view complete patient medical records, edit analysis reports, and initiate collaborative consultations; patients can only view their own medical information and health advice, and cannot access other patients' data, ensuring data privacy. It also features graphic and text display, prominent highlighting, and voice broadcasting functions: Analysis results are displayed using a combination of text and charts (such as using line graphs to show indicator trends), with abnormal indicators or important suggestions highlighted in red font, exclamation marks, etc., allowing users to quickly grasp key information; voice broadcasting is supported, allowing patients to hear health advice via voice, improving ease of use.

[0037] 5. Medical Information Security Protection Module

[0038] This module is crucial for protecting patient medical data privacy and system security. Its specific functions include:

[0039] Data encryption: Industry-leading encryption algorithms are used to encrypt medical data during transmission (such as from camera module to storage module, from local system to public medical resource platform) and storage to prevent data from being illegally stolen or tampered with.

[0040] User Access Management: Establish a comprehensive user access system, including identity authentication. Users must authenticate their identity through methods such as account password, facial recognition, or fingerprint recognition. After successful authentication, different permissions are assigned based on user type (e.g., doctor, nurse, patient, administrator). For example, administrators can set system parameters and manage user permissions; doctors can view the files of their assigned patients and edit analysis reports; patients can only view their personal information, ensuring that each user can only access data within their authorized scope and preventing data leaks.

[0041] Operation Log Recording: The system automatically records all user operations, including login time, operation content (such as viewing files, uploading reports, modifying analysis results), and operating devices, forming a complete operation log. In the event of a data security incident, the responsible party can be traced through the operation log, which also facilitates system security auditing and risk assessment.

[0042] 6. Multi-terminal adaptation module

[0043] This module resolves compatibility issues between different devices, enabling seamless system use across multiple terminals. Specific functions are as follows:

[0044] Cross-platform framework support: The system is developed based on cross-platform development frameworks (such as React Native and Flutter), ensuring that the system can run stably on different operating systems such as Windows, macOS, iOS, and Android. There is no need to develop separate versions for different terminals, reducing development and maintenance costs.

[0045] Adaptive Design: The system supports adaptive design, automatically adjusting the interface layout, font size, button position, etc., according to the screen size of the terminal device (such as small mobile phone screen, large tablet screen, wide screen computer screen) to ensure good display effect and operation experience on different devices.

[0046] Multi-terminal data synchronization: Enables real-time data synchronization across multiple terminals. Operations performed by a user on one terminal (such as uploading reports or modifying health reminder settings) can be synchronized to other logged-in terminals in real time, ensuring consistent information access across different devices and improving ease of use. It is particularly suitable for touchscreen devices such as tablets and mobile phones, facilitating quick operations by physicians in the clinic or allowing patients to view health information anytime on their mobile devices, promoting interaction between physicians and patients.

[0047] 7. Health Reminder Module

[0048] This module provides users with personalized health management support, helping them to monitor their health status in a timely manner. Specific functions are as follows:

[0049] Personalized reminder generation: The system generates personalized health reminders based on the user's medical information (such as abnormal indicators in test reports and past medical history) and health goals (such as "controlling blood pressure" and "regular check-ups"). For example, for patients with hypertension, a reminder is generated to "monitor blood pressure daily and take antihypertensive medication on time"; for patients who need follow-up examinations, a reminder is generated to "7 days until the next blood routine check-up".

[0050] Multiple reminder methods supported: Supports various reminder methods, including system message push, SMS reminder, telephone reminder, email reminder, etc. Users can choose the reminder method and frequency according to their own habits (such as once a day or once a week) to ensure that they do not miss important health matters.

[0051] 8. Medical Resource Matching Module

[0052] This module integrates medical resources, providing users with a convenient channel to access medical services. Specific functions are as follows:

[0053] Medical resource information integration: Integrate information on medical institutions at all levels (such as tertiary hospitals and community health service centers) and doctors (including professional titles, professional fields, and consultation hours) to form a medical resource database. Users can use the system to search for nearby or suitable medical institutions and doctors.

[0054] Online appointment function: Provides an online appointment portal, allowing users to directly book appointments and examinations (such as gastroscopy) within the system without being redirected to other platforms, simplifying the appointment process and saving time. Attached Figure Description

[0056] Figure 1 System architecture diagram, showing the data flow and collaboration relationships between modules. Detailed Implementation

[0059] To make the technical solution of the present invention clearer and easier to understand, the implementation process of the system is described in detail below in conjunction with specific application scenarios.

[0060] Scenario 1: Community hospital doctor's outpatient clinic scenario

[0061] Preliminary preparations: The outpatient clinic of the community hospital is equipped with a medical terminal with a top rotating camera and a bottom scanning module. AI analysis models for common diseases (such as hypertension and colds) and a basic patient database are deployed locally. Doctors log in to their exclusive accounts (including the ability to view files and initiate collaboration).

[0062] Information collection: When a patient (e.g., Mr. Zhang, 45 years old, complaining of headache) comes to the doctor, the doctor enters the patient's name through the terminal and retrieves his past records (including blood pressure records from six months ago); the paper blood pressure test report of the day (systolic blood pressure 145 mmHg) is placed into the lower scanning module, and the system automatically scans, corrects and uploads the data to the records.

[0063] AI Analysis and Diagnosis: The system calls the local AI model to analyze the current abnormal blood pressure (above the normal range) and past data, and initially judges that "high blood pressure may cause headaches"; if the doctor needs further confirmation, the latest hypertension diagnosis and treatment guidelines can be retrieved through the public medical resource module, and the doctor can be advised to "follow a low-salt diet and have a follow-up examination in 3 days". The diagnosis and treatment plan will be pushed to the patient's mobile phone through the interactive module, and a follow-up examination reminder will be set.

[0064] Scenario 2: Home-based health management for patients with chronic diseases

[0065] Device configuration: Diabetic patients (such as 60-year-old Ms. Li) install the home version of the system APP on their mobile phones, bind their personal information, connect the APP to the home Bluetooth blood glucose meter, and enable the mobile phone camera as a simple scanning tool.

[0066] Data Upload: Aunt Li measures her blood sugar after each meal according to the health reminders. The data is automatically synchronized to the APP via Bluetooth and uploaded to her file. She scans and uploads her paper glycated hemoglobin report (e.g., 7.5%) with her mobile phone camera every month. The APP generates a trend chart.

[0067] Monitoring and Interaction: If blood glucose exceeds 9 mmol / L for two consecutive days, the system's local AI model will trigger an alert and send an SMS reminder to Ms. Li to adjust her diet. Ms. Li can consult her attending physician about dietary issues through the APP. After reviewing her recent data, the doctor will provide suggestions and help her book a follow-up appointment for next month through the medical resource matching module.

Claims

1. A medical assistance and information service system based on artificial intelligence, characterized in that, include: a. Camera scanning and uploading module: Includes a camera component that can be flexibly positioned (top, bottom, or both), supporting multi-angle scanning of paper test reports and medical images, and synchronously uploading recognition information; b. Patient information classification and storage module: Automatically classifies medical information by patient name and creates / supplements individual patient files; c. Artificial Intelligence Interpretation, Analysis and Recommendation Module: Contains two sub-modules that can run independently or in conjunction, which analyze uploaded reports and generate medical interpretations and recommendations; c1. Private Medical Database and Model Module: Locally deployed medical database and specialized AI model, supporting independent operation and ensuring data privacy; c2. Public Medical Resources and Collaboration Module: Access internet medical resources and support medical data sharing and cross-institutional collaboration; d. User interaction module: Supports operations by medical practitioners, patients, etc., displays analysis results and receives feedback.

2. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, The camera scanning and uploading module adopts high-definition acquisition technology. The top camera can be a rotating camera or a camera with a prism to capture various test reports. The top and bottom cameras can be adapted to the scanning needs of different usage scenarios. It has automatic image correction and clarity optimization functions and supports batch recognition and uploading.

3. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, The patient information classification and storage module can automatically extract patient information and classify newly uploaded medical information into the corresponding file. If a dedicated file for a patient does not exist, it will automatically create a dedicated file named after the patient and store the medical information uploaded this time in it. It also has an information backup function to prevent data loss and supports users to quickly retrieve the corresponding file information by keywords such as patient name and upload time.

4. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, The private medical database and model module prioritizes the use of local mature databases and specialized models to conduct multi-dimensional basic analysis on the textual information, numerical information and image information of medical images in the test report, identify abnormal detection indicators and abnormal areas in the images, make preliminary judgments on abnormal characteristics and potential health problems, generate basic interpretations that comply with medical standards, and ensure the accuracy and security of the analysis results. The public medical resources and collaboration module, based on the results of the basic analysis, supplements and retrieves the latest medical information from the internet to optimize and improve the conclusions of the basic analysis. At the same time, it enables data sharing and collaboration functions in combination with user needs (such as cross-team consultations and regional medical collaboration), supporting multiple parties to participate in the analysis process and generating more comprehensive medical recommendations that are in line with the latest medical advancements, including suggestions for further examinations, lifestyle adjustments, and recommended medical departments. Moreover, the entire analysis process and the basis for the recommendations are traceable, making it easy for users to understand the analysis logic.

5. The artificial intelligence-based medical assistance and information service system according to claim 4, characterized in that, The AI ​​interpretation, analysis, and suggestion module supports differentiated analysis of different types of test reports: For blood test reports, the private medical database and model module focuses on calling local standard databases and analysis models such as blood routine, biochemical indicators, and immune indicators to interpret the deviation of values ​​from the normal range and their clinical significance; the public medical resources and collaboration module supplements the analysis by calling the latest research results on blood test indicators and special reference standards for different populations (such as the elderly and pregnant women), improving the analysis conclusions, and supporting doctors across departments to share blood test data and jointly judge health problems; For imaging examination reports, the private medical database and model module focuses on calling the standard database of imaging structure and lesion recognition model to identify structural abnormalities, lesion location, size, morphology and other features in the images; the public medical resources and collaboration module supplements the information by calling the latest guidelines for imaging diagnosis and case data of rare lesions to help improve diagnostic accuracy, and supports radiologists and clinicians to collaborate on editing image interpretation reports to achieve efficient connection between diagnosis and treatment recommendations; Meanwhile, this module has the ability to update data and models periodically or in real time, and can be optimized and adjusted based on user feedback. Both modules can also adjust parameters based on user feedback (such as suggestions from medical professionals to correct analysis results) to adapt to the needs of different application scenarios.

6. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, The user interaction module uses a graphical interface, supports multi-terminal login, provides exclusive functions for different users, and features graphic display, eye-catching notes, and voice broadcast functions.

7. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, It includes a medical information security protection module, which employs data encryption, user access control (including identity authentication), and operation log recording functions.

8. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, It includes a multi-terminal adaptation module, which achieves seamless adaptation based on a cross-platform framework. It supports adaptive design and multi-terminal data synchronization, and is especially suitable for use with touch screen devices, facilitating interaction between doctors and patients.

9. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, It includes a health reminder module that generates personalized health reminders and supports multiple reminder methods, which users can choose themselves.

10. The artificial intelligence-based medical assistance and information service system according to claim 1, characterized in that, It includes a medical resource matching module, which integrates medical resource information, provides recommendations for medical institutions and doctors, online appointment portals, and communication and training resources for medical practitioners.

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