Intelligent outpatient service management system and method based on artificial intelligence

Abstract

The invention relates to the technical field of intelligent outpatient service management, and discloses an intelligent outpatient service management system and method based on artificial intelligence, and the system comprises the following subsystems: an intelligent reservation system, an intelligent hospital guide system, an intelligent diagnosis auxiliary system, an intelligent medicine management system, an intelligent follow-up visit service system, and a data analysis and mining system. According to the intelligent diagnosis assisting system, the intelligent reservation system and the intelligent diagnosis guiding system can provide personalized doctor recommendation and preliminary diagnosis suggestions according to the illness state and symptoms of the patient, so that the doctor seeing experience better meeting the requirements of the patient is provided, and the intelligent diagnosis assisting system achieves the intelligent diagnosis assisting effect through the technologies of medical image recognition, medical record data analysis and the like. The system and the method assist doctors in diagnosis and treatment decision making and provide more accurate auxiliary information and personalized treatment schemes, so that the doctors are helped to make correct diagnosis more quickly, and the diagnosis and treatment efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent outpatient management, in particular to an intelligent outpatient management system and method based on artificial intelligence. BACKGROUND

[0002] The intelligent outpatient management system is a medical information system designed to improve the efficiency and quality of outpatient services, including functions such as registration management, medical record management, doctor scheduling, diagnosis and treatment assistance, pharmacy management, financial management, and data statistics and analysis.

[0003] After searching, application number CN202310680553.5 discloses an intelligent outpatient management system and method based on artificial intelligence. The intelligent outpatient management system is connected to the official website of the hospital through an API interface, real-time access to the hospital's outpatient resource information, and intelligent management of the outpatient resource information, including intelligent scheduling, appointment registration, doctor workstation, and medical data analysis. Patients enter the intelligent outpatient management system and input their personal information and medical needs. The intelligent outpatient management system recommends appropriate outpatient resources based on the patient's personal information and medical needs through an artificial intelligence recommendation module. The invention can reasonably allocate hospital outpatient resources through intelligent scheduling and appointment registration functions, avoiding long waiting times for patients and wasting of idle resources. Since the system can allocate resources based on patient needs and hospital resources, it can effectively reduce patient waiting times and improve the medical experience.

[0004] The above prior art has relatively simple functions, lacks personalized recommendations, intelligent diagnostic assistance, and other important functions, and therefore has certain deficiencies in improving medical service levels and patient medical experience. Therefore, we propose an intelligent outpatient management system and method based on artificial intelligence. SUMMARY

[0005] (I) Technical problems solved

[0006] To address the deficiencies in the prior art, the present application provides an intelligent outpatient management system and method based on artificial intelligence.

[0007] (II) Technical solutions

[0008] To achieve the above-mentioned purposes, the present application provides the following technical solutions: an intelligent outpatient management system based on artificial intelligence, comprising the following subsystems:

[0009] Intelligent appointment system:

[0010] The intelligent appointment system intelligently matches patients with doctors based on their medical conditions, doctors' specialties, and available time, providing the best appointment time and doctor selection. The system uses natural language processing and machine learning techniques to analyze patients' medical condition descriptions and recommend suitable specialist doctors, optimizing appointment periods and visit times.

[0011] Intelligent guidance system:

[0012] The AI-based guidance system provides preliminary diagnosis and recommendations based on patients' symptoms and medical history, guiding them to the correct department and doctor. The system combines augmented reality (AR) technology to identify patients' symptoms in real-time through their phone cameras and provides relevant medical advice and guidance, improving patients' self-diagnosis capabilities.

[0013] Intelligent diagnosis assistance system:

[0014] The system assists doctors in diagnosis and treatment decisions through medical image recognition and medical record data analysis. It incorporates deep learning and medical knowledge graphs to provide more accurate assistance information and personalized treatment plans, helping doctors make correct diagnoses more quickly.

[0015] Intelligent drug management system:

[0016] The system intelligently recommends drugs and provides medication advice based on patients' medical conditions, allergy history, and other information. It uses blockchain technology to ensure drug traceability and safety, ensuring patient safety.

[0017] Intelligent follow-up service system:

[0018] The system tracks and manages patients' medical conditions through remote monitoring and intelligent reminders, detecting abnormalities in a timely manner. It uses natural language generation technology to automatically generate personalized health advice and lifestyle guidance, helping patients better manage their health.

[0019] Data analysis and mining system:

[0020] The system analyzes patients' medical records and visit data to identify potential disease trends and treatment patterns, providing data support for hospital decision-making. It uses federated learning and privacy computing technology to securely share and collaboratively analyze multi-hospital data, improving data utilization efficiency while protecting patient privacy.

[0021] Preferably, in the intelligent appointment system, the system first needs to collect the patient's condition description, the doctor's expertise, and the available time information, which is obtained through the patient's filled-in form, medical records, doctor's schedule, etc. The collected data needs to be cleaned, standardized and stored. The natural language text of the patient's condition description is processed and analyzed to extract keywords, entities and semantic information. NLP technology is used to help the system understand the patient's description of symptoms, diseases and needs. The information of the doctor's expertise and experience is extracted to construct the doctor's feature vector, including the doctor's professional field, treatment experience and patient evaluation. Based on the collected data and feature vector, a machine learning model is established, which is a classification model or a regression model.

[0022] Preferably, in the intelligent appointment system, the intelligent matching is performed by using the machine learning model, and the most suitable specialist doctor is recommended for the patient according to the patient's description of the condition, the doctor's expertise and the available time, etc. The system optimizes the appointment period and the visit time through the patient's visit time preference and the doctor's schedule. The system provides a user interface for the patient to fill in the condition description and select the visit time information. The patient can view the doctor and visit time recommended by the system and adjust according to personal needs. The system learns and optimizes based on user feedback and behavior, continuously improving the accuracy of recommendations and user experience.

[0023] Preferably, in the intelligent diagnosis system, AR technology is used to let the patient use the camera of the mobile phone or other device to take pictures of his own symptoms or lesions. Real-time image recognition technology can visualize the patient's symptoms, allowing doctors to more intuitively understand the patient's condition, thereby improving the accuracy and efficiency of diagnosis. The system uses computer vision technology to analyze and recognize the symptom images taken by the patient, extracting key information. This image recognition technology can help doctors better understand the patient's condition. The system comprehensively analyzes the symptom images obtained through AR technology and the text description provided by the patient to further optimize the diagnosis results, improve the accuracy and comprehensiveness of the diagnosis, and provide more accurate diagnosis recommendations for the patient. The intelligent diagnosis system provides an intuitive and simple user interface, allowing patients to easily upload symptom images and fill in symptom description information.

[0024] Preferably, in the intelligent diagnosis assistance system, the system uses computer vision technology to analyze and recognize medical images, such as X-rays, CT scans, and MRIs. A deep learning model is used to train for automatic detection of abnormal areas or features in the images. The system extracts key information from the patient's electronic medical record, including medical history, laboratory test results, and symptom description. Data mining and natural language processing techniques are used to structure and semantically understand the medical record data to extract effective diagnostic information.

[0025] Preferably, in the intelligent diagnostic assistance system, a diagnostic model for a specific disease is established using deep learning technology. The model analyzes medical images and medical record data to assist doctors in diagnosing diseases and provide preliminary diagnostic results or suggestions. The system uses a medical knowledge graph to store and organize knowledge in the medical field, including diseases, symptoms, and treatment options. During diagnosis, the system queries the knowledge graph based on the patient's clinical information and medical image results to obtain relevant medical knowledge and provide personalized treatment recommendations. Medical image recognition, medical record data analysis, and deep learning models are integrated into a unified platform to achieve comprehensive diagnostic assistance. The system provides personalized diagnostic assistance information based on the characteristics of different cases and the knowledge in the medical knowledge graph to help doctors make correct diagnoses and treatment decisions more quickly.

[0026] Preferably, in the intelligent drug management system, the system establishes a drug knowledge base, including information on the composition, efficacy, side effects, and interactions of drugs. The information is obtained through medical databases and drug instructions. Based on the patient's condition and allergy history, the system analyzes the patient's medication needs using expert systems or machine learning algorithms. The system intelligently recommends appropriate drugs based on the patient's individual characteristics and condition, and provides medication recommendations, including dosage, usage, and precautions. The system records drug information on the blockchain to form an immutable distributed database. Drug information includes production batch, manufacturer, and distribution link. When patients purchase drugs, the system verifies the source and authenticity of the drugs through blockchain technology to ensure drug quality and safety. Blockchain technology supports tracing the production, circulation, and use of drugs, helping regulatory authorities and patients understand the entire life cycle of drugs. Patients can view detailed drug information, verify drug authenticity, and provide feedback and evaluations in the system.

[0027] Preferably, in the intelligent follow-up service system, the system monitors patients' physiological parameters and health data in real time through medical devices or wearable devices. The devices transmit patients' data to the system backend for storage and analysis. The system can monitor patients' health status in real time. The system generates reminder information based on patients' health data and medical plans and reminds patients to take medication, perform tests, or visit doctors on time through mobile applications, SMS, and email. Reminder content includes medication reminders, test reminders, and appointment reminders. The system analyzes patients' physiological parameters and health data to detect abnormalities in a timely manner. When the system detects abnormalities, it automatically generates an alarm and notifies the patient and their medical team. The system generates personalized health recommendations and lifestyle guidance using natural language generation technology based on patients' health data and personal characteristics. Health recommendation content includes dietary recommendations, exercise recommendations, and lifestyle guidance.

[0028] Preferably, in the data analysis and mining system, clustering analysis, association rule mining and prediction modeling methods are used to mine valuable information and rules from data, federal learning technology is introduced to realize safe sharing and cooperative analysis of multi-hospital data, avoid privacy leakage risk caused by data centralization, in federal learning, each hospital keeps the data local, only shares model parameters or gradients, and does not share original data, thereby protecting patient privacy, the system introduces privacy computing technology to encrypt sensitive data or use secure multi-party computing technology to ensure data privacy during the calculation process.

[0029] A method for intelligent outpatient management based on artificial intelligence, comprising the following steps:

[0030] Step 1: intelligently match the best treatment time and doctor selection according to the patient's condition, doctor's expertise and available time, optimize the appointment and treatment time;

[0031] Step 2: Provide preliminary diagnosis and advice based on patient symptoms and medical history, guide patients to the correct department and doctor for treatment;

[0032] Step 3: Use medical image recognition and medical record data analysis technology to assist doctors in diagnosis and treatment decision-making;

[0033] Step 4: Intelligently recommend drugs and provide drug use advice according to the patient's condition and allergy history, and use blockchain technology to ensure drug safety;

[0034] Step 5: Through remote monitoring and intelligent reminders, the system tracks and manages the patient's condition, and timely detects abnormal conditions;

[0035] Step 6: Analyze patient medical records and treatment data to find potential disease trends and treatment patterns, and provide data support for hospital decision-making.

[0036] (Three) beneficial effects

[0037] Compared with the prior art, the present application provides an intelligent outpatient management system and method based on artificial intelligence, which has the following beneficial effects:

[0038] 1、The intelligent outpatient management system and method based on artificial intelligence, the intelligent appointment system and the intelligent diagnosis system can provide personalized doctor recommendation and preliminary diagnosis advice according to the patient's condition and symptoms, so as to provide a medical experience that meets the patient's needs, the intelligent diagnosis auxiliary system assists the doctor in diagnosis and treatment decision-making through medical image recognition, medical record data analysis and other technologies, provides more accurate auxiliary information and personalized treatment plan, so as to help the doctor make correct diagnosis more quickly, and improve the diagnosis and treatment efficiency.

[0039] 2、The intelligent outpatient management system and method based on artificial intelligence, the intelligent drug management system intelligently recommends drugs and provides medication advice according to the patient's condition and allergy history and other information, and combines blockchain technology to ensure the traceability and safety of the source of drugs, thereby ensuring the safety of patient medication, the intelligent follow-up service system tracks and manages the patient's condition through remote monitoring and intelligent reminders, discovers abnormal conditions in a timely manner, and generates personalized health advice and lifestyle guidance through natural language generation technology, helping patients better manage their health, and the data analysis and mining system analyzes patient medical records, medical data and other information to discover potential disease trends and diagnosis and treatment patterns, providing data support for hospital decision-making, thereby improving the efficiency of hospital management and the scientific nature of decision-making. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0041] Embodiment: An intelligent outpatient management system based on artificial intelligence, comprising the following subsystems:

[0042] Intelligent appointment system:

[0043] The intelligent appointment system intelligently matches according to the patient's condition, the doctor's specialty and the available time, and provides the best appointment time and doctor selection; the system uses natural language processing and machine learning technology, and the system recommends suitable specialist doctors for the patient through analysis of the patient's condition description, and optimizes the appointment period and appointment time;

[0044] Intelligent guidance system:

[0045] The intelligent guidance system based on artificial intelligence provides preliminary diagnosis and suggestions according to the patient's symptoms and medical history information, guides the patient to the correct department and doctor for treatment, and the system combines augmented reality (AR) technology, the system identifies the patient's symptoms in real time through the camera of the patient's mobile phone, and then provides relevant medical advice and guidance to improve the patient's self-diagnosis ability;

[0046] Intelligent diagnosis assistance system:

[0047] The system assists doctors in diagnosis and treatment decision-making through medical image recognition, medical record data analysis and other technologies, and the system introduces deep learning and medical knowledge graph, the system provides more accurate auxiliary information and personalized treatment plans in the diagnosis process to help doctors make correct diagnosis more quickly;

[0048] Intelligent medicine management system:

[0049] The system intelligently recommends medicines and provides medication advice based on patient's medical history, allergy history, etc. Combined with blockchain technology, the system can ensure the traceability and safety of medicines, ensuring patient safety.

[0050] Intelligent follow-up service system:

[0051] Through remote monitoring and intelligent reminders, the system tracks and manages the patient's condition, and timely detects abnormal conditions. Using natural language generation technology, the system automatically generates personalized health advice and lifestyle guidance to help patients better manage their health.

[0052] Data analysis and mining system:

[0053] Through analysis of patient medical records, medical data and other information, the system discovers potential disease trends and treatment patterns, providing data support for hospital decision-making. By introducing federated learning and privacy computing technology, the system realizes safe sharing and cooperative analysis of multi-hospital data, improving data utilization efficiency while protecting patient privacy.

[0054] In the intelligent appointment system, the system first needs to collect information about the patient's condition, the doctor's expertise, and the available time. These information is obtained through patient-filled forms, medical records, and doctor's schedules. The collected data needs to be cleaned, standardized, and stored. The natural language text of the patient's condition description is processed and analyzed to extract keywords, entities, and semantic information. NLP technology is used to help the system understand the patient's symptoms, diseases, and needs. The doctor's expertise and experience information is extracted to construct the doctor's feature vector, including the doctor's professional field, treatment experience, and patient evaluation. Based on the collected data and feature vectors, a machine learning model is established, which can be a classification model or a regression model.

[0055] In the intelligent appointment system, machine learning models are used for intelligent matching. Based on the patient's condition description, the doctor's expertise, and the available time, the system recommends the most suitable specialist for the patient. The system optimizes the appointment time and visit time by considering the patient's visit time preference and the doctor's schedule. The system provides a user interface for patients to fill in the condition description and select the visit time. Patients can view the recommended doctors and visit times and make adjustments according to their individual needs. The system learns and optimizes based on user feedback and behavior, continuously improving the accuracy of recommendations and user experience.

[0056] In the intelligent diagnosis system, through AR technology, patients can use the camera of their mobile phones or other devices to take pictures of their symptoms or lesions. Real-time image recognition technology can visualize the symptoms on the patient's body, allowing doctors to more intuitively understand the patient's condition, thereby improving the accuracy and efficiency of diagnosis. The system uses computer vision technology to analyze and recognize the symptom images taken by the patient, extracting key information. This image recognition technology can help doctors more comprehensively understand the patient's condition. The system combines the symptom images obtained through AR technology with the text descriptions provided by the patient for comprehensive analysis, further optimizing the diagnosis results and improving the accuracy and comprehensiveness of diagnosis, providing more accurate diagnosis recommendations for patients. The intelligent diagnosis system provides an intuitive and simple user interface, allowing patients to easily upload symptom images and fill out symptom description information.

[0057] In the intelligent diagnosis system, through AR technology, patients can use the camera of their mobile phones or other devices to take pictures of their symptoms or lesions. Real-time image recognition technology can visualize the symptoms on the patient's body, allowing doctors to more intuitively understand the patient's condition, thereby improving the accuracy and efficiency of diagnosis. The system uses computer vision technology to analyze and recognize the symptom images taken by the patient, extracting key information. This image recognition technology can help doctors more comprehensively understand the patient's condition. The system combines the symptom images obtained through AR technology with the text descriptions provided by the patient for comprehensive analysis, further optimizing the diagnosis results and improving the accuracy and comprehensiveness of diagnosis, providing more accurate diagnosis recommendations for patients. The intelligent diagnosis system provides an intuitive and simple user interface, allowing patients to easily upload symptom images and fill out symptom description information.

[0058] In the intelligent diagnosis system, through AR technology, patients can use the camera of their mobile phones or other devices to take pictures of their symptoms or lesions. Real-time image recognition technology can visualize the symptoms on the patient's body, allowing doctors to more intuitively understand the patient's condition, thereby improving the accuracy and efficiency of diagnosis. The system uses computer vision technology to analyze and recognize the symptom images taken by the patient, extracting key information. This image recognition technology can help doctors more comprehensively understand the patient's condition. The system combines the symptom images obtained through AR technology with the text descriptions provided by the patient for comprehensive analysis, further optimizing the diagnosis results and improving the accuracy and comprehensiveness of diagnosis, providing more accurate diagnosis recommendations for patients. The intelligent diagnosis system provides an intuitive and simple user interface, allowing patients to easily upload symptom images and fill out symptom description information.

[0059] In the intelligent medicine management system, the system establishes a medicine knowledge base, including the information of the composition, efficacy, side effects and interactions of the medicine, which is obtained through the channels of medical database and medicine instruction manual. Based on the information of the patient's condition and allergy history, the system analyzes the patient's medication needs using expert system or machine learning algorithm. The system intelligently recommends suitable medicine according to the individual characteristics and condition of the patient, and provides medication advice, including the dosage, usage and precautions of the medicine. The system records the information of the medicine on the blockchain to form an unalterable distributed database, which includes the production batch, manufacturer and distribution link. When the patient purchases the medicine, the system verifies the source and authenticity of the medicine through blockchain technology to ensure the quality and safety of the medicine. The blockchain technology supports the tracing of the production, circulation and use process of the medicine, helping the regulatory authorities and patients understand the whole life cycle of the medicine. The patient can check the detailed information of the medicine, verify the authenticity of the medicine and provide feedback and evaluation in the system.

[0060] In the intelligent follow-up service system, the system monitors the physiological parameters and health data of the patient in real time through medical devices or wearable devices. The devices transmit the patient's data to the system background for storage and analysis. The system can monitor the health status of the patient in real time. The system intelligently generates reminder information according to the health data and medical plan of the patient, and reminds the patient to take the medicine, perform the detection or go to the hospital on time through the mobile application, SMS and email. The reminder content includes medicine reminder, detection reminder and appointment reminder. The system can detect abnormal conditions in time by analyzing the physiological parameters and health data of the patient. When the system detects abnormal conditions, it will automatically generate an alarm and notify the patient and his medical team. The system generates personalized health advice and lifestyle guidance using natural language generation technology according to the health data and personal characteristics of the patient. The health advice content includes diet suggestion, exercise suggestion and lifestyle guidance.

[0061] In the data analysis and mining system, clustering analysis, association rule mining and prediction modeling methods are used to mine valuable information and rules from data. Federal learning technology is introduced to realize safe sharing and cooperative analysis of multi-hospital data, avoiding the privacy leakage risk caused by data centralization. In federal learning, each hospital keeps the data local and only shares model parameters or gradients without sharing original data, thus protecting patient privacy. The system introduces privacy computing technology to encrypt sensitive data or use secure multi-party computing technology to ensure data privacy during the calculation process.

[0062] A method for intelligent outpatient management based on artificial intelligence, comprising the following steps:

[0063] First step: Intelligent matching of the best appointment time and doctor based on patient condition, doctor expertise and available time, optimizing appointment and visit time;

[0064] Second step: Providing preliminary diagnosis and recommendations based on patient symptoms and medical history, guiding patients to the correct department and doctor for treatment;

[0065] Third step: Using medical image recognition and medical record data analysis technology to assist doctors in diagnosis and treatment decision-making;

[0066] Fourth step: Intelligent drug recommendation and medication advice based on patient condition and allergy history, combined with blockchain technology to ensure drug safety;

[0067] Fifth step: Through remote monitoring and intelligent reminders, the system tracks and manages patient conditions, and timely detects abnormal conditions;

[0068] Sixth step: Analyzing patient medical records and treatment data to identify potential disease trends and treatment patterns, providing data support for hospital decision-making.

[0069] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based intelligent outpatient management system, characterized in that, The system includes the following subsystems: Intelligent appointment system: The intelligent appointment system intelligently matches patients with doctors based on factors such as patient condition, doctor's specialty, and available time, providing the best appointment time and doctor selection. The system uses natural language processing and machine learning techniques to analyze patient condition descriptions and recommend appropriate specialist doctors, and optimizes appointment time and visit time. Intelligent guidance system: The AI-based guidance system provides preliminary diagnosis and recommendations based on patient symptoms and medical history information, guiding patients to the correct department and doctor for treatment. The system combines augmented reality (AR) technology to identify patient symptoms in real time through the patient's phone camera, then provides relevant medical advice and guidance, improving patient self-diagnosis capabilities. Intelligent diagnosis assistance system: The system uses medical image recognition and medical record data analysis to assist doctors in diagnosis and treatment decision-making. The system incorporates deep learning and medical knowledge graphs to provide more accurate auxiliary information and personalized treatment plans, helping doctors make correct diagnoses more quickly. Intelligent drug management system: The system intelligently recommends drugs and provides medication recommendations based on patient condition, allergy history, and other information. Combined with blockchain technology, the system ensures traceability and safety of drug sources, ensuring patient medication safety. Intelligent follow-up service system: Through remote monitoring and intelligent reminders, the system tracks and manages patient conditions, detects abnormalities in a timely manner, and uses natural language generation technology to automatically generate personalized health recommendations and lifestyle guidance, helping patients better manage their health. Data analysis and mining system: Through analysis of patient medical records, visit data, and other information, the system identifies potential disease trends and treatment patterns, providing data support for hospital decision-making. The system uses federated learning and privacy computing technology to enable secure sharing and collaborative analysis of multi-hospital data, improving data utilization efficiency while protecting patient privacy.

2. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the intelligent appointment system, the system first needs to collect patient condition descriptions, doctor's specialty, and available time information. These information is obtained through patient-filled forms, medical records, and doctor's schedule, etc. The collected data needs to be cleaned, standardized and stored. The natural language text of patient condition description is processed and analyzed to extract keywords, entities and semantic information. NLP technology is used to help the system understand the patient's symptoms, diseases and needs. The doctor's specialty and experience information is extracted to construct the doctor's feature vector, including the doctor's professional field, treatment experience and patient evaluation. Based on the collected data and feature vector, a machine learning model is established, which can be a classification model or a regression model.

3. The intelligent outpatient management system based on artificial intelligence according to claim 2, characterized in that, The intelligent appointment system uses a machine learning model for intelligent matching, recommends the most suitable specialist doctor for the patient according to the patient's description of the illness, the doctor's specialty and available time, and optimizes the appointment time and treatment time through the patient's treatment time preference and the doctor's scheduling situation. The system provides a user interface for the patient to fill in the illness description and select the treatment time information. The patient can view the recommended doctors and treatment times by the system and make adjustments according to personal needs. The system learns and optimizes according to user feedback and behavior, continuously improving the accuracy of recommendations and user experience.

4. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the intelligent diagnosis system, AR technology is used to let patients use their mobile phones or other devices to take pictures of their symptoms or lesions. Real-time image recognition technology can visualize the patient's symptoms, allowing doctors to more intuitively understand the patient's condition and improve the accuracy and efficiency of diagnosis. The system uses computer vision technology to analyze and identify the symptom images taken by the patient, extracting key information. This image recognition technology can help doctors better understand the patient's condition. The system combines the symptom images obtained through AR technology with the patient's written description for comprehensive analysis, further optimizing the diagnosis results and improving the accuracy and comprehensiveness of diagnosis, providing more accurate guidance suggestions for patients. The intelligent diagnosis system provides an intuitive and simple user interface, allowing patients to easily upload symptom images and fill in symptom description information.

5. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the intelligent diagnosis assistance system, the system uses computer vision technology to analyze and identify medical images, specifically X-rays, CT scans, and MRIs. A deep learning model is used to automatically detect abnormal areas or features in the images. The system extracts key information from the patient's electronic medical record, including medical history, laboratory test results, and symptom description. Data mining and natural language processing techniques are used to structure and semantically understand the medical record data to extract effective diagnostic information.

6. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the intelligent diagnosis assistance system, deep learning technology is used to establish a diagnosis model for specific diseases. The model analyzes medical images and medical record data to assist doctors in diagnosing diseases and provides preliminary diagnosis results or suggestions. The system uses a medical knowledge graph to store and organize knowledge in the medical field, including diseases, symptoms, and treatment plans. During the diagnosis process, the system queries the knowledge graph based on the patient's clinical information and medical image results to obtain relevant medical knowledge and provide personalized treatment recommendations. Medical image recognition, medical record data analysis, and deep learning models are integrated into a unified platform to achieve comprehensive diagnosis assistance functions. The system provides personalized diagnosis assistance information for doctors based on the characteristics of different cases and the knowledge in the medical knowledge graph, helping them make correct diagnosis and treatment decisions more quickly.

7. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, The intelligent medicine management system, the system establishes a medicine knowledge base, including the information of the composition, efficacy, side effects and interaction of the medicine, the information is obtained through the channels of medical database and medicine instruction manual, based on the information of patient's condition and allergy history, the system analyzes the patient's medication needs by using expert system or machine learning algorithm, the system intelligently recommends suitable medicine according to the individual characteristics and condition of the patient, and provides medication advice, including the dosage, usage and precautions of the medicine, the system records the information of the medicine on the blockchain, forming an unalterable distributed database, the medicine information includes production batch, manufacturer and distribution link, when the patient purchases the medicine, the system verifies the source and authenticity of the medicine through the blockchain technology, ensuring the quality and safety of the medicine, the blockchain technology supports the tracing of the production, circulation and use process of the medicine, helping the regulatory department and the patient to understand the whole life cycle of the medicine, the patient can check the detailed information of the medicine in the system, verify the authenticity of the medicine, and provide feedback and evaluation.

8. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the intelligent follow-up service system, the system monitors the physiological parameters and health data of the patient in real time through medical devices or wearable devices, the device transmits the patient's data to the system background for storage and analysis, the system can monitor the health status of the patient in real time, the system intelligently generates reminder information according to the health data and medical plan of the patient, and reminds the patient to take the medicine, detect or go to the hospital on time through the mobile application, SMS and email, the reminder content includes medicine reminder, detection reminder and appointment reminder, the system can detect abnormal conditions in time by analyzing the physiological parameters and health data of the patient, when the system detects abnormal conditions, it will automatically generate an alarm and notify the patient and his medical team, the system generates personalized health advice and lifestyle guidance by using natural language generation technology according to the health data and personal characteristics of the patient, the health advice content includes diet suggestion, exercise suggestion and lifestyle guidance.

9. The intelligent outpatient management system based on artificial intelligence according to claim 1, characterized in that, In the data analysis and mining system, the methods of clustering analysis, association rule mining and prediction modeling are used to mine valuable information and rules from data, federal learning technology is introduced to realize safe sharing and cooperative analysis of multi-hospital data, and privacy leakage risk caused by data centralization is avoided, in federal learning, each hospital keeps the data local and only shares model parameters or gradients without sharing original data, so as to protect patient privacy, the system introduces privacy computing technology to encrypt sensitive data or use secure multi-party computing technology to ensure the privacy of data in the computing process. 10.A method for intelligent outpatient management based on artificial intelligence, characterized in that, The steps include: First step: intelligently match the best appointment time and doctor selection according to the patient's condition, doctor's expertise and available time, optimize the appointment and visit time; Second step: provide preliminary diagnosis and suggestion based on patient's symptoms and medical history, guide the patient to the correct department and doctor for treatment; Third step: use medical image recognition and medical record data analysis technology to assist doctors in diagnosis and treatment decision; Fourth step: intelligently recommend medicine and provide medication advice according to the patient's condition and allergy history, ensure medicine safety combined with blockchain technology; Step 5: Through remote monitoring and intelligent reminders, the system tracks and manages the patient's condition, and timely discovers abnormal conditions; Step 6: Analyze patient medical records and medical data to find potential disease trends and treatment patterns, and provide data support for hospital decision-making.