AI service development system based on agent technology

Abstract

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical health, in particular to an AI service system based on agent technology. BACKGROUND

[0002] Medical health service refers to a series of professional services provided for individuals or groups, aiming to prevent, diagnose, treat diseases and improve health conditions. These services cover medical consultation, examination, treatment, rehabilitation and other aspects, aiming to maintain and improve people's physical and mental health level. With the development of computer technology, medical health service system emerges as the times require.

[0003] Medical health service system refers to a comprehensive platform that integrates medical resources and provides efficient and convenient medical health service support for medical institutions, medical staff and patients by using information technology means, usually including electronic medical record management, medical image processing, remote medical treatment, health data management and other modules. Through information means, the platform realizes the rapid transmission and sharing of medical data, reduces repeated examination, shortens the waiting time of patients, cooperates with automated and intelligent auxiliary diagnosis tools, which can help doctors make diagnosis and treatment decisions more quickly and accurately, effectively improving the efficiency and quality of medical services. At the same time, it can optimize the allocation of medical resources, accurately predict medical needs through big data analysis, and reasonably allocate medical resources to alleviate the problem of tight medical resources.

[0004] However, in practical application, although there are some intelligent auxiliary diagnosis tools at present, the intelligent level of medical health service system as a whole still needs to be improved, especially in the diagnosis and treatment decision support of complex diseases, the accuracy and reliability of intelligent system are still not high, so it is difficult to provide accurate and efficient diagnosis services for patients. SUMMARY

[0005] The purpose of the present application is to provide an AI service system based on agent technology to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical scheme: an AI service system based on agent technology, comprising:

[0007] Medical professional module: responsible for providing professional knowledge support related to medical health, including clinical manifestations, laboratory examination, diagnostic criteria, prognosis stratification and treatment plan, and constantly learning and updating medical knowledge base to ensure the accuracy and reliability of diagnosis; in actual operation, the medical professional module constructs a multi-dimensional medical knowledge graph by using semantic network, which contains 3.8 million medical entities and 52 million relationship edges, as well as a dynamically updated clinical path library (covering 98% of ICD-11 diseases);

[0008] Digital Avatar Module: responsible for creating digital avatars for medical staff, used for simulation therapy, personalized medical plan development, etc.

[0009] Continuous Optimization Module: responsible for monitoring system performance, including performance indicators of each module, user behavior, system response time, etc., collecting user feedback, including satisfaction surveys, error reports, improvement suggestions, etc., continuously optimizing algorithms and models through a closed-loop feedback mechanism, allowing the system to continuously learn and improve, and the optimization results will replace or update the original system modules after deployment;

[0010] Interactive Service Module: responsible for providing user-friendly interfaces, supporting multiple interaction methods such as voice and text, making it easy for patients and medical staff to use;

[0011] Security Management Module: responsible for data security, privacy protection, and access control of the system;

[0012] Ethics and Compliance Management Module: monitors system usage, combining ethical and compliance requirements to ensure compliance with medical ethics and legal requirements;

[0013] Basic Service Module: responsible for integrating medical resources, providing electronic medical record management, medical image processing, remote medical services, and other basic medical services;

[0014] Intelligent Diagnosis Module: responsible for using machine learning and deep learning technologies to analyze medical data in depth, providing auxiliary diagnosis for complex diseases.

[0015] Further, the digital avatar module uses data modeling and simulation technology to achieve accurate construction and dynamic updating of digital avatars, including the following sub-modules:

[0016] Data Collection Sub-module: responsible for obtaining relevant data of medical staff from various channels (hospital information systems, electronic medical record systems, medical image systems, etc.), including personal information, professional experience, skill level, historical diagnosis and treatment records, and other multi-dimensional data, and performing data cleaning, denoising, standardization, and other preprocessing, i.e., using data cleaning algorithms and tools to remove invalid, duplicate, and incorrect data, and then performing standardization processing to make it meet the requirements of subsequent modeling and simulation;

[0017] Digital Avatar Modeling Sub-module: based on pre-processed data, use machine learning or deep learning algorithms to build a digital avatar model of medical staff, which accurately reflects the real characteristics and capabilities of medical staff;

[0018] Model optimization and updating submodule: according to the performance of medical staff in actual work and new data, constantly optimize and update the digital avatar model, ensure that the model continuously reflects the latest state and ability of medical staff, specific operation: regularly collect new data of medical staff, such as new diagnosis and treatment records, skill improvement, etc., and input new data into the model, update and optimize the model, specific use online learning, incremental learning and other technologies to realize real-time update and optimization of the model.

[0019] Further, the operation steps of the digital avatar modeling submodule are as follows:

[0020] Step S1, the core goal of digital avatar (such as prediction, classification, data generation, etc.) and the type of input / output data (structured, time series, image, etc.) are determined, and the appropriate algorithm and model architecture are selected, the selection criteria include data size, computing resources, real-time requirements, interpretability requirements, etc.; The candidate algorithms in this step include: neural network (suitable for complex nonlinear relationship, such as CNN processing image, RNN / LSTM processing time series data, Transformer processing multi-modal data), decision tree / random forest (suitable for structured data classification or regression with strong interpretability), support vector machine (SVM, suitable for small sample high-dimensional data classification), clustering algorithm (such as K-means for data grouping in unsupervised scenario), Bayesian model (processing probability reasoning problem);

[0021] Step S2, input the preprocessed data (which has completed cleaning, denoising, standardization, etc.) into the algorithm, and perform model training and optimization, specifically: divide the preprocessed data into training set, validation set and test set (70%-15%-15%), then input the data in batches, adjust the model parameters through back propagation (neural network) or iterative optimization (such as node splitting of decision tree), and monitor the loss function (such as cross entropy, MSE) and validation set performance, the model optimization includes hyperparameter tuning (using grid search, random search or Bayesian optimization to adjust key parameters such as neural network hidden layer size, Dropout rate), regularization (to prevent overfitting: L1 / L2 regularization, early stopping, data augmentation);

[0022] Step S3, verify the effectiveness and accuracy of the model by cross-validation and index evaluation method, and save it in standardized format (such as ONNX, PMML) for deployment, wherein K-fold cross-validation (such as K=5) is used to ensure the stability of the model, and the evaluation index includes:

[0023] Classification task: accuracy, precision, recall, F1 score, AUC-ROC;

[0024] Regression tasks: Mean Squared Error (MSE), R 2 Scores;

[0025] Generation models: SSIM (image similarity), BLEU (text generation).

[0026] Further, the closed-loop feedback mechanism adjusts the input or operation through the feedback loop according to the difference between the system output result and the actual target, and continuously approaches and reaches the desired target state.

[0027] Further, the interactive service module specifically provides the following interaction modes:

[0028] Voice interaction mode: integrate speech recognition and synthesis technology, users use voice to dialogue with the system, the system recognizes the user's voice command, and gives the corresponding voice reply or executes the related operation;

[0029] Text interaction mode: the system provides a user-friendly text input box and chat interface, users input text through the keyboard to communicate with the system, the system analyzes the user's text input, and gives the corresponding text reply or executes the instruction;

[0030] Graphical interface interaction mode: the system provides intuitive interaction elements such as buttons, menus, icons, etc. through the graphical user interface (GUI), users interact with the system through mouse or touch screen operations such as clicking and dragging;

[0031] Multimedia interaction mode: the system supports uploading, displaying and playing multimedia content such as pictures, videos and audios, users upload medical images, audios or videos to communicate with the system, and the system also displays the diagnosis results or medical advice in multimedia form.

[0032] Further, the security management module adopts a layered protection system to ensure the security and privacy of medical data, including data layer security, application layer security, and network layer security, as follows:

[0033] 1) Data layer security:

[0034] For structured data (electronic medical record), field-level AES-256 encryption (such as patient name, ID number), key managed by HSM (hardware security module);

[0035] For unstructured data (image / report), file-level transparent encryption (such as Linux eCryptfs), encryption key bound to user identity;

[0036] And the following privacy protection strategies are adopted for all data:

[0037] Differential privacy: add Laplace noise to statistical query results (such as clinical research data release);

[0038] k-anonymity: Ensure that each record is indistinguishable from at least k-1 other records (e.g. after de-identification of medical records: [Age: 30-35 years old, District: Beijing Chaoyang District]);

[0039] 2) Application layer security: Adopt multi-factor authentication (MFA) method, i.e. doctor end needs digital certificate + fingerprint, patient end needs mobile phone number + OTP + behavior verification;

[0040] 3) Network layer security:

[0041] Medical special network isolation: Divide virtual network domain through VXLAN (e.g. image storage network segment is isolated from the Internet);

[0042] Intrusion detection: Deploy Suricata based on rule (e.g. DICOM protocol abnormal flow) and AI model to detect attacks.

[0043] Further, the ethical and compliance requirements of the ethical and compliance management module specifically include:

[0044] Medical ethics and moral standards: Require medical personnel to uphold high professional ethics and respect patients' dignity and privacy;

[0045] Legal and regulatory compliance requirements: Clearly define the laws and regulations that must be followed during the operation of the medical system, including but not limited to medical dispute handling, medical accident reporting, drug management, etc;

[0046] Medical responsibility definition mechanism: Clearly define the rights and responsibilities of medical personnel in medical behavior, providing a clear basis for the handling of medical disputes;

[0047] Patient privacy protection standards: Require medical personnel to strictly protect patients' privacy information and not to disclose or misuse;

[0048] Data usage authorization management: Require the medical system to obtain explicit authorization from patients when collecting, using, storing and sharing patient data, and to follow strict data protection procedures;

[0049] Medical dispute handling mechanism: Establish a fair and efficient medical dispute handling process, providing a platform for resolving conflicts and seeking justice for both doctors and patients;

[0050] Ethical review process: Require the medical system to undergo ethical committee review and approval when conducting research, testing or new technology application involving humans or animals;

[0051] Informed consent management: Require medical personnel to fully inform patients of their condition, treatment options, risks and alternatives before implementing medical behavior, and obtain their explicit consent.

[0052] Further, the service types provided by the basic service module include electronic medical record management, medical image processing, remote medical treatment, and resource scheduling, and the specific functions of each service type are as follows:

[0053] Electronic medical record management: multi-modal medical record structuring (text / speech / image), cross-institutional medical record sharing and tracing, intelligent retrieval and abstract generation;

[0054] Medical image processing: DICOM image intelligent analysis, three-dimensional reconstruction and surgical planning, image quality control and report generation;

[0055] Remote medical treatment: 5G consultation platform, Internet of Things device access, emergency command system;

[0056] Resource scheduling: intelligent bed matching, examination device reservation optimization, specialist doctor recommendation.

[0057] Further, the intelligent diagnosis module is composed of a clinical feature extractor (which converts raw medical data into a standardized feature vector), a multi-modal data fusioner (cross-modal feature alignment and fusion), a diagnosis reasoning engine (which generates differential diagnosis and confidence evaluation), a treatment optimizer (which generates personalized treatment plans), and an explanatory engine (which provides understandable decision basis).

[0058] Further, the auxiliary diagnosis of the intelligent diagnosis module includes the following steps:

[0059] Step S1, feature extraction: the clinical feature extractor converts raw medical data (such as medical records, image data, laboratory test results, etc.) into a standardized feature vector. This process includes data cleaning, preprocessing, and feature selection and extraction, aiming to convert complex and diverse medical data into a form that models can understand;

[0060] Step S2, multi-modal fusion: the multi-modal data fusioner aligns and fuses cross-modal features from different sources, considering multiple types of data such as text, images, and numerical values to obtain more comprehensive patient health information;

[0061] Step S3, diagnosis reasoning

[0062] Knowledge graph retrieval: the diagnosis reasoning engine searches for similar historical cases, diagnosis standards, treatment plans, and other information in the medical knowledge base, providing a basis for diagnosis reasoning;

[0063] Deep learning prediction: the diagnosis reasoning engine applies a deep learning model to predict the fused feature vector, generating differential diagnosis and confidence evaluation;

[0064] Step S4, treatment optimization

[0065] Drug conflict detection: the treatment optimizer checks whether there are conflicts or adverse reactions between drugs in the generated treatment plan, ensuring the safety of the treatment plan;

[0066] Dose calculation: the treatment optimizer calculates and adjusts the dose of the drug according to the specific situation of the patient (such as weight, age, gender, etc.), to ensure the maximization of the treatment effect;

[0067] Step S5, explanation generation: the explanatory engine converts the results of diagnostic reasoning and treatment optimization into structured reports, including diagnostic conclusions, treatment plans, decision-making basis, etc. This step improves the transparency and intelligibility of medical services, allowing patients and medical staff to better understand the diagnostic process and treatment plan.

[0068] The present application provides an AI service system based on agent technology, which has the following beneficial effects:

[0069] 1. The system uses semantic web to build a multi-dimensional medical knowledge graph, which contains a large number of medical entities and relationship edges, as well as a dynamically updated clinical pathway library, and can provide accurate and reliable medical knowledge support, cooperate with the intelligent diagnosis module to analyze medical data in depth, provide auxiliary diagnosis for complex diseases, improve the accuracy and efficiency of diagnosis, and has high intelligence level, especially in the aspect of complex disease diagnosis and treatment decision support, can provide accurate and efficient diagnosis service for patients.

[0070] 2. The system creates digital avatars for medical staff for simulation treatment, personalized medical plan development, etc. The digital avatar model can accurately reflect the real characteristics and abilities of medical staff, and through continuous optimization and updating, the model continuously reflects the latest state and ability of medical staff, thereby helping to optimize the treatment plan and improve the treatment effect.

[0071] 2. The system has strong security management capability, adopts a layered protection system to ensure the security and privacy of medical data, including data layer security, application layer security and network layer security, and combines strict data privacy protection strategies such as differential privacy and k-anonymity, to further protect the privacy information of patients. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 The logic diagram of the AI service system based on agent technology of the present application;

[0073] Figure 2 The operation step schematic diagram of the digital avatar modeling sub-module of the AI service system based on agent technology of the present application;

[0074] Figure 3An auxiliary diagnosis step schematic diagram of an intelligent diagnosis module of an AI service system based on an agent technology;

[0075] Figure 4 A service providing mechanism schematic diagram of an AI service system based on an agent technology. DETAILED DESCRIPTION

[0076] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present application, but cannot be used to limit the scope of the present application.

[0077] As shown in Figures 1-4 , an AI service system based on an agent technology includes:

[0078] Medical professional module: provides professional knowledge support related to medical health, including clinical manifestations, laboratory examinations, diagnostic criteria, prognosis stratification, and treatment plans, and continuously learns and updates the medical knowledge base to ensure the accuracy and reliability of diagnosis. In this embodiment, the medical professional module builds a multi-dimensional medical knowledge graph by utilizing semantic networks, which contains 3.8 million+ medical entities and 52 million+ relationship edges, as well as a dynamically updated clinical pathway library (covering 98% of ICD-11 diseases).

[0079] Digital avatar module: creates a digital avatar for medical personnel for simulating treatment and developing personalized medical plans. This module realizes the precise construction and dynamic updating of the digital avatar through data modeling and simulation technology.

[0080] Data acquisition sub-module: responsible for obtaining relevant data of medical personnel from various channels (hospital information system, electronic medical record system, medical imaging system, etc.), including personal information, professional experience, skill level, historical diagnosis and treatment records, and multi-dimensional data, and pre-processing the collected data, such as cleaning, denoising, and standardization, i.e. using data cleaning algorithms and tools to remove invalid, duplicate, and incorrect data, and then standardizing the data to meet the requirements of subsequent modeling and simulation.

[0081] Digital avatar modeling sub-module: based on the pre-processed data, uses machine learning or deep learning algorithms to construct a digital avatar model of medical personnel to accurately reflect the real characteristics and capabilities of medical personnel. As shown in Figure 2 , the operation steps of this sub-module are as follows:

[0082] Step S1, determine the core goal of digital duplication (such as prediction, classification, data generation, etc.), and the type of input / output data (structured, time series, image, etc.), according to which the appropriate algorithm and model architecture are selected, and the selection criteria include data size, computing resources, real-time requirements, interpretability requirements, etc. The candidate algorithms in this step include: neural networks (suitable for complex nonlinear relationships, such as CNN for image processing, RNN / LSTM for time series data, and Transformer for multi-modal data), decision trees / random forests (suitable for structured data classification or regression with strong interpretability), support vector machines (SVM, suitable for small sample high-dimensional data classification), clustering algorithms (such as K-means for data grouping in unsupervised scenarios), Bayesian models (for handling probability inference problems);

[0083] Step S2, input the preprocessed data (cleaning, denoising, standardization, etc. have been completed) into the algorithm, and perform model training and optimization. Specifically, divide the preprocessed data into training set, validation set and test set (70%-15%-15%), then input the data in batches, adjust the model parameters through back propagation (neural network) or iterative optimization (such as node splitting of decision tree), and monitor the loss function (such as cross-entropy, MSE) and validation set performance. Model optimization includes hyperparameter tuning (using grid search, random search or Bayesian optimization to adjust key parameters such as neural network hidden layer size, Dropout rate), regularization (to prevent overfitting: L1 / L2 regularization, Early Stopping, data augmentation);

[0084] Step S3, verify the effectiveness and accuracy of the model through cross-validation and index evaluation methods, and save it in a standardized format (such as ONNX, PMML) for deployment. Cross-validation uses K-fold cross-validation (such as K=5) to ensure model stability, and the evaluation index includes:

[0085] Classification task: accuracy, precision, recall, F1 score, AUC-ROC;

[0086] Regression task: Mean Squared Error (MSE), R 2 Score;

[0087] Generation model: SSIM (image similarity), BLEU (text generation).

[0088] In this embodiment, taking user behavior prediction as an example, the operation is as follows:

[0089] Select algorithm: use LSTM neural network to process user time series behavior data;

[0090] Training: input historical click stream data to predict the next operation;

[0091] Optimization: Reduce overfitting by Dropout and adjust the number of LSTM layers;

[0092] Validation: 5-fold cross-validation shows accuracy 85%, F1 score 0.82;

[0093] Deployment: Integrated into the recommendation system to provide real-time services.

[0094] Model optimization and update submodule: According to the performance of medical staff in actual work and new data, constantly optimize and update the digital avatar model, ensure that the model continuously reflects the latest state and ability of medical staff, specific operation: regularly collect new data of medical staff, such as new diagnosis and treatment records, skill improvement, etc., and input new data into the model, update and optimize the model, use online learning, incremental learning and other technologies to realize real-time update and optimization of the model.

[0095] Continuous optimization module: Monitor system performance, including performance indicators of each module, user behavior, system response time, etc., collect user feedback, including satisfaction survey, error report, improvement suggestion, etc., through closed-loop feedback mechanism to continuously optimize algorithm and model, make system continuous learning and progress, and the optimization results will replace or update the original system module after deployment. Among them, the closed-loop feedback mechanism adjusts the input or operation through the feedback loop according to the difference between the system output result and the actual target, and continuously approaches and reaches the expected target state.

[0096] Interactive service module: Provide user-friendly interface, support multiple interaction modes such as voice, text, etc., convenient for patients and medical staff to use.

[0097] In this embodiment, the interactive service module specifically provides the following interaction modes:

[0098] Voice interaction mode: Integrate speech recognition and synthesis technology, users use voice to dialogue with the system, the system recognizes the user's voice command, and gives the corresponding voice reply or executes the related operation. This mode makes users no need to manually input, especially in the case of busy or inconvenient with both hands, can more conveniently communicate with the system, at the same time, also increases the usability and affinity of the system, makes the user feel like talking to a real assistant;

[0099] Text interaction mode: The system provides a user-friendly text input box and chat interface, users communicate with the system through keyboard input, the system analyzes the user's text input and gives corresponding text replies or executes instructions. This mode has high flexibility and accuracy, users can edit and modify their input at any time to ensure accurate information transmission, and it is also convenient to save and view historical records for future reference or sharing;

[0100] Graphical interface interaction mode: The system provides intuitive interaction elements such as buttons, menus, icons, etc. through the graphical user interface (GUI), users interact with the system through mouse or touch screen operations such as clicking and dragging. This mode makes the operation more intuitive and easy to understand, especially for users who are not familiar with text input or voice interaction. Through the graphical interface, users can quickly find the required functions or information, improving operation efficiency;

[0101] Multimedia interaction mode: The system supports uploading, displaying and playing multimedia content such as pictures, videos and audio. Users communicate with the system by uploading medical images, recordings or videos, and the system displays diagnostic results or medical advice in multimedia form. This mode enriches the content form of communication, making the transmission of medical information more vivid and comprehensive. For example, doctors can make more accurate diagnoses by viewing patient-uploaded medical images, and patients can learn more medical knowledge by watching health education videos provided by the system.

[0102] Security management module: responsible for data security, privacy protection and access control of the system.

[0103] In this embodiment, the security management module adopts a layered protection system to ensure the security and privacy of medical data, including data layer security, application layer security and network layer security, as follows:

[0104] 1) Data layer security:

[0105] For structured data (electronic medical record), field-level AES-256 encryption (such as patient name, ID number), key managed by HSM (hardware security module);

[0106] For unstructured data (images / reports), file-level transparent encryption (such as Linux eCryptfs), encryption key bound to user identity;

[0107] And the following privacy protection strategies are adopted for all data:

[0108] Differential privacy: add Laplace noise to statistical query results (such as clinical research data release);

[0109] k-anonymization: Ensure that each record is indistinguishable from at least k-1 other records (e.g. after de-identification of medical records: [Age: 30-35 years old, District: Beijing Chaoyang District]).

[0110] 2) Application layer security: Adopt multi-factor authentication (MFA) method, i.e. doctor end needs digital certificate + fingerprint, patient end needs mobile phone number + OTP + behavior verification.

[0111] 3) Network layer security:

[0112] Medical professional network isolation: Divide virtual network domain through VXLAN (e.g. image storage network segment is isolated from the Internet);

[0113] Intrusion detection: Deploy Suricata based on rule (e.g. DICOM protocol abnormal flow) and AI model to detect attacks.

[0114] Ethics and compliance management module: Monitor the use of the system, combined with ethical and compliance requirements, to ensure compliance with medical ethics and legal requirements. Ethical and compliance requirements include:

[0115] Medical ethics and moral standards: Require medical staff to uphold high professional ethics and respect patients' dignity and privacy;

[0116] Legal and regulatory compliance requirements: Clearly define the laws and regulations that must be followed during the operation of the medical system, including but not limited to medical dispute resolution, medical accident reporting, drug management, etc.;

[0117] Medical responsibility definition mechanism: Clearly define the rights and responsibilities of medical staff in medical behavior, providing a clear basis for medical dispute resolution;

[0118] Patient privacy protection standards: Require medical staff to strictly protect patients' privacy information and not to disclose or misuse;

[0119] Data usage authorization management: Requires the medical system to obtain explicit authorization from patients when collecting, using, storing and sharing patient data, and to follow strict data protection procedures;

[0120] Medical dispute resolution mechanism: Establish a fair and efficient medical dispute resolution process to provide a platform for resolving conflicts and seeking justice for both doctors and patients;

[0121] Ethical review process: Requires the medical system to undergo ethical committee review and approval when conducting research, testing or new technology applications involving humans or animals;

[0122] Informed consent management: Requires medical staff to fully inform patients of their condition, treatment options, risks and alternatives before implementing medical procedures and obtaining their explicit consent.

[0123] Basic service module: Integrates medical resources, provides electronic medical record management, medical image processing, remote medical care, resource scheduling, and other basic medical services. In this embodiment, the service providing mechanism of this module is as shown in Figure 4 , and the specific functions of each service are as follows:

[0124] Electronic medical record management: Multi-modal medical record structuring (text / speech / image), cross-institutional medical record sharing and tracing, intelligent retrieval and summary generation;

[0125] Medical image processing: DICOM image intelligent analysis, three-dimensional reconstruction and surgical planning, image quality control and report generation;

[0126] Remote medical care: 5G consultation platform, Internet of Things device access, emergency command system;

[0127] Resource scheduling: Intelligent bed matching, examination device reservation optimization, specialist doctor recommendation.

[0128] Intelligent diagnosis module: Utilizing machine learning and deep learning techniques, it conducts deep analysis on medical data and provides auxiliary diagnosis for complex diseases.

[0129] In this embodiment, the intelligent diagnosis module is composed of a clinical feature extractor (which converts raw medical data into a standardized feature vector), a multi-modal data fusioner (cross-modal feature alignment and fusion), a diagnosis reasoning engine (which generates differential diagnosis and confidence assessment), a treatment optimizer (which generates personalized treatment plans), and an explanatory engine (which provides understandable decision basis), as shown in Figure 3 , the auxiliary diagnosis of this module includes the following steps:

[0130] Step S1, feature extraction: The clinical feature extractor converts raw medical data (such as medical records, image data, laboratory test results, etc.) into a standardized feature vector. This process includes data cleaning, preprocessing, and feature selection and extraction, aiming to convert complex and diverse medical data into a form understandable by the model;

[0131] Step S2, multi-modal fusion: The multi-modal data fusioner aligns and fuses cross-modal features from different sources, considering various types of data such as text, images, and numerical values to obtain more comprehensive patient health information;

[0132] Step S3, diagnosis reasoning

[0133] Knowledge graph retrieval: The diagnosis reasoning engine searches the medical knowledge base for similar historical cases, diagnosis standards, treatment plans, and other information related to the current case, providing a basis for diagnosis reasoning;

[0134] Deep learning prediction: the diagnostic reasoning engine applies a deep learning model to the fused feature vectors to make predictions and generate differential diagnoses along with confidence assessments;

[0135] Step S4, treatment optimization

[0136] Drug conflict detection: the treatment optimizer checks the generated treatment plan for potential drug conflicts or adverse reactions, ensuring the safety of the treatment plan;

[0137] Dose calculation: the treatment optimizer calculates and adjusts the dosage of the drugs according to the specific conditions of the patient (such as weight, age, gender, etc.), to ensure the maximization of treatment effect;

[0138] Step S5, explanation generation: the explanatory engine converts the results of diagnostic reasoning and treatment optimization into structured reports, including diagnostic conclusions, treatment plans, decision-making basis, etc. This step improves the transparency and understandability of medical services, allowing patients and medical staff to better understand the diagnostic process and treatment plan.

[0139] Embodiments of the present application are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the application to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are chosen and described in order to best explain the principles of the application and its practical application, and to enable others skilled in the art to understand the application in order to design various embodiments with various modifications for specific uses.

Claims

1. An AI service system based on intelligent agent technology, characterized in that, The system includes a medical professional module, a digital avatar module, a continuous optimization module, an interactive service module, a security management module, an ethics and compliance management module, a basic service module, and an intelligent diagnosis module. The medical professional module is responsible for providing professional knowledge support related to medical health, and continuously learning and updating the medical knowledge base. The digital avatar module is responsible for creating digital avatars for medical staff, which are used for simulation treatment and personalized medical plan development. The continuous optimization module is responsible for monitoring system performance, collecting user feedback, and continuously optimizing algorithms and models through a closed-loop feedback mechanism to enable the system to continuously learn and improve. The interactive service module is responsible for providing a user-friendly interface that supports multiple interaction methods to facilitate the use of patients and medical staff. The security management module is responsible for data security, privacy protection, and access control of the system. The ethics and compliance management module monitors the use of the system, combines ethical and compliance requirements, and ensures compliance with medical ethics and legal requirements. The basic service module is responsible for integrating medical resources, providing electronic medical record management, medical image processing, and remote medical basic services. The intelligent diagnosis module is responsible for using machine learning and deep learning techniques to analyze medical data in depth and provide auxiliary diagnosis for complex diseases.

2. The AI service system based on the agent technology according to claim 1, wherein The digital avatar module uses data modeling and simulation technology to accurately construct and dynamically update digital avatars, including the following sub-modules: Data collection sub-module: responsible for obtaining relevant data of medical staff from various channels, including personal information, professional experience, skill level, and historical diagnosis and treatment records, and preprocessing the collected data; Digital avatar modeling sub-module: based on the preprocessed data, use machine learning or deep learning algorithms to build a digital avatar model of medical staff to accurately reflect the real characteristics and abilities of medical staff; Model optimization and update sub-module: continuously optimize and update the digital avatar model based on the performance of medical staff in actual work and newly generated data to ensure that the model continuously reflects the latest status and abilities of medical staff. 3.The AI service system based on the agent technology of claim 2, wherein, The operation steps of the digital avatar modeling sub-module are as follows: Step S1, clearly define the core target of the digital avatar and the type of input / output data, and select the appropriate algorithm and model architecture accordingly, including data size, computing resources, real-time requirements, and interpretability requirements; candidate algorithms in this step include neural networks, decision trees / random forests, support vector machines, clustering algorithms, and Bayesian models; Step S2, input the preprocessed data into the algorithm for model training and optimization, specifically: divide the preprocessed data into training set, validation set and test set, then input the data in batches, adjust the model parameters through back propagation or iterative optimization, and monitor the loss function and validation set performance; model optimization includes hyperparameter tuning and regularization; Step S3, verify the effectiveness and accuracy of the model through cross-validation and index evaluation methods, and save it in a standardized format for deployment, where K-fold cross-validation is used for cross-validation, and evaluation metrics include: Classification tasks: accuracy, precision, recall, F1 score, AUC-ROC; Regression task: Mean squared error, R 2 Score; Generation models: SSIM, BLEU. 4.The AI service system based on the agent technology of claim 1, wherein, The closed-loop feedback mechanism adjusts the input or operation through the feedback loop according to the difference between the system output result and the actual target, and continuously approaches and reaches the expected target state. 5.The AI service system based on the agent technology of claim 1, wherein, The interactive service module specifically provides the following interaction modes: Voice interaction mode: users use voice to have a conversation with the system, the system recognizes the user's voice instructions, and gives corresponding voice replies or performs related operations; Text interaction mode: the system provides a text input box and a chat interface, users input text through the keyboard to communicate with the system, the system analyzes the user's text input, and gives corresponding text replies or executes instructions; Graphical interface interaction mode: the system provides intuitive interaction elements through the graphical user interface, users interact with the system through mouse or touch screen operations; Multimedia interaction mode: the system supports uploading, displaying and playing multimedia content, users communicate with the system by uploading medical images, audio or video, and the system also displays diagnosis results or medical advice in multimedia form. 6.The AI service system based on the agent technology of claim 1, wherein, The security management module adopts a layered protection system, including data layer security, application layer security, and network layer security, as follows: 1) Data layer security: For structured data, field-level AES-256 encryption, key managed by HSM; For unstructured data, file-level transparent encryption, encryption key bound to user identity; And the following privacy protection strategies are adopted for all data: Differential privacy: add Laplace noise to statistical query results; k-anonymity: ensure that each record is indistinguishable from at least k-1 other records; 2) Application layer security: adopt multi-factor authentication, i.e. digital certificate + fingerprint for doctor side, and mobile phone number + OTP + behavior verification for patient side; 3) Network layer security: Medical special network isolation: virtual network domain is divided by VXLAN; Intrusion detection: Suricata is deployed based on rules and AI model to detect attacks. 7.The AI service system based on the agent technology of claim 1, wherein, The ethical and compliance requirements of the ethics and compliance management module specifically include: Medical ethics and moral standards: require medical staff to uphold high professional ethics and respect patients' dignity and privacy; Legal and regulatory compliance requirements: clearly define the laws and regulations that must be followed during the operation of the medical system, including but not limited to medical dispute handling, medical accident reporting, drug management, etc.; Medical responsibility definition mechanism: clearly defines the rights and responsibilities of medical staff in medical behavior, providing a clear basis for medical dispute resolution; Patient privacy protection standards: require medical staff to strictly protect patients' privacy information and not to disclose or misuse it; Data usage authorization management: requires the medical system to obtain explicit authorization from patients when collecting, using, storing and sharing patient data, and to follow strict data protection procedures; Medical dispute handling mechanism: establishes a medical dispute handling process, providing a platform for resolving conflicts and seeking justice for both doctors and patients; Ethical review process: requires the medical system to undergo ethical committee review and approval when conducting research, testing or new technology application involving humans or animals; Informed consent management: requires medical staff to fully inform patients of the disease, treatment options, risks and alternative options before implementing medical behavior, and obtain the patient's explicit consent. 8.The AI service system based on the agent technology of claim 1, wherein, The basic service module provides service types including electronic medical record management, medical image processing, remote medical treatment and resource scheduling, and the specific functions of each service type are as follows: Electronic medical record management: multi-modal medical record structuring, cross-institutional medical record sharing and tracing, intelligent retrieval and abstract generation; Medical image processing: DICOM image intelligent analysis, three-dimensional reconstruction and surgery planning, image quality control and report generation; Remote medical treatment: 5G consultation platform, Internet of Things device access, emergency command system; Resource scheduling: intelligent bed matching, examination equipment reservation optimization, specialist doctor recommendation. 9.The AI service system based on the agent technology of claim 1, wherein, The intelligent diagnosis module is composed of a clinical feature extractor, a multi-modal data fusioner, a diagnosis reasoning engine, a treatment optimizer and an explanatory engine. 10.The AI service system based on the agent technology of claim 1, wherein, The auxiliary diagnosis of the intelligent diagnosis module includes the following steps: Step S1, feature extraction: the clinical feature extractor converts the original medical data into a standardized feature vector, which includes data cleaning, preprocessing, and feature selection and extraction; Step S2, multi-modal fusion: the multi-modal data fusioner performs cross-modal feature alignment and fusion on the feature vectors from different sources to obtain more comprehensive patient health information; Step S3, diagnosis reasoning Knowledge graph retrieval: the diagnosis reasoning engine searches the medical knowledge base for similar historical cases, diagnosis standards and treatment options for the current case; Deep learning prediction: the diagnosis reasoning engine applies a deep learning model to predict the fused feature vector and generate a differential diagnosis and confidence assessment; Step S4, treatment optimization Drug conflict detection: the treatment optimizer checks the generated treatment plan for drug conflicts or adverse reactions; Dose calculation: the treatment optimizer calculates and adjusts the dose of the drug according to the patient's specific situation to maximize the treatment effect; Step S5, explanation generation: the explanatory engine converts the results of diagnosis reasoning and treatment optimization into a structured report, including diagnosis conclusion, treatment plan and decision basis.

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