Oral medical information interaction method and system based on AI intelligent agent

Abstract

The invention provides an oral medical information interaction method and system based on an AI intelligent agent, and the method comprises the steps: obtaining local oral specialist knowledge data of an oral doctor, carrying out the fusion processing of the local oral specialist knowledge data and a general artificial intelligence language model, and constructing an oral specialist AI intelligent agent; constructing an information interaction carrier based on the oral specialist AI intelligent agent, so that a user can initiate an oral medical consultation request, performing semantic analysis on the user consultation request, generating oral medical consultation response information, receiving and analyzing a tooth image, a medication-related image or oral diagnosis text data submitted by the user, and sending the oral medical consultation response information to the user; outputting a corresponding oral medical information analysis result; data generated in the interaction process are stored and analyzed, the subsequent oral medical information interaction process is optimized according to the analysis result, closed-loop processing of oral medical consultation, self-service analysis and interaction optimization is achieved, and the intelligent level and service efficiency of oral medical information interaction are improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence and oral medical information interaction technology, and in particular to an oral medical information interaction method and system based on an AI agent. Background Technology

[0002] Oral healthcare services are characterized by high consultation frequency, diverse problem types, and a high barrier to understanding professional terminology. When patients experience tooth pain, gum abnormalities, post-operative care, medication consultation, or need interpretation of examination reports, they typically hope to receive timely, accurate, and easily understandable oral healthcare information before their visit. Currently, oral healthcare institutions mainly rely on human customer service, immediate doctor responses, or their own online platforms for consultation services. These methods are generally limited by staff scheduling and working hours, making it difficult to achieve a continuous and stable response. Furthermore, different staff members may offer varying interpretations of the same consultation questions, making it difficult to establish a unified and reusable standardized response mechanism, resulting in insufficient service consistency.

[0003] With the development of artificial intelligence technology, some technical solutions have attempted to introduce general intelligent question-answering models for medical consultation. However, these general models lack constraints on the boundaries of oral specialty knowledge and the context of diagnosis and treatment, making it difficult for the output content to fit specific oral medical scenarios and localized treatment processes, resulting in insufficient professional relevance. Furthermore, existing solutions typically only support single-type information processing, such as text consultations or single-image recognition, making it difficult to uniformly receive, parse, and interpret multiple types of oral medical-related data, such as dental images, medication information, and diagnostic reports, within the same interactive process. Therefore, we propose an oral medical information interaction method and system based on AI intelligent agents. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an AI-based oral medical information interaction method and system, thereby resolving the technical problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for oral medical information interaction based on AI agents includes the following steps: S1. The local oral specialty knowledge base corresponding to the dentist is fused with the general artificial intelligence language model. Based on the fusion result, an oral specialty AI agent for information interaction in oral medical scenarios is generated. The local oral specialty knowledge base includes at least oral disease diagnosis and treatment guidelines, oral medication instructions, oral treatment item descriptions, and doctor practice-related information. S2. Encapsulate the dental AI agent into an information interaction carrier that can be accessed and forwarded by users on social platforms, so that the information interaction carrier has both the ability to display dental medical information and the information interaction entry point based on the dental AI agent. S3. Receive oral medical consultation requests initiated by users through information interaction carriers, call oral specialist AI intelligent agents to parse the oral medical consultation requests and generate corresponding oral medical consultation response information, and synchronously output oral medical service guidance information related to the consultation content in the oral medical consultation response information. S4. Receive oral medical data submitted by users through information interaction carriers, and parse the oral medical data based on the oral specialty AI agent to generate oral medical information parsing results corresponding to the oral medical data. The oral medical data includes at least tooth image data, oral medication related image data or oral diagnosis related text data. S5. Store user consultation data, interaction response data, and oral medical information analysis results to form a user oral medical information interaction history record, and adjust and optimize the response content and service guidance methods in subsequent oral medical information interaction processes based on the user oral medical information interaction history record.

[0006] S1 specifically involves: acquiring local oral specialty knowledge data corresponding to at least one dentist, including data on oral disease diagnosis and treatment guidelines, oral medication instructions, oral treatment procedure descriptions, and physician practice-related information; inputting the local oral specialty knowledge data into a general artificial intelligence language model for fusion processing, enabling the general artificial intelligence language model to possess specialized semantic understanding capabilities in the field of oral medicine while retaining natural language interaction capabilities; and generating an oral specialty AI agent for oral medical information interaction based on the fusion processing results, which is used for consultation analysis and information output in subsequent oral medical information interaction processes.

[0007] S2 specifically involves: constructing an information interaction carrier based on the dental AI agent to support it, enabling the carrier to invoke the dental AI agent; binding dental medical information display content corresponding to the dental AI agent to the information interaction carrier, including dental medical service descriptions, doctor's practice information, and dental medical consultation portal information; and making the information interaction carrier accessible to users through social platforms, allowing users to initiate dental medical information interaction requests through the carrier.

[0008] S3 specifically involves: receiving oral healthcare consultation requests initiated by users through an information interaction medium, with the requests being input in text form; calling upon an oral healthcare AI agent to perform semantic analysis on the oral healthcare consultation request and determine the oral healthcare consultation content corresponding to the request; generating corresponding oral healthcare consultation response information based on the analysis results, and outputting the oral healthcare consultation response information to the user through the information interaction medium, while also outputting oral healthcare service guidance information associated with the consultation content.

[0009] S4 specifically involves: receiving oral healthcare-related data submitted by users through an information interaction platform; calling upon an oral healthcare AI agent to parse the oral healthcare-related data and generate oral healthcare information parsing results corresponding to the data, including dental image data, oral medication-related image data, or oral diagnosis-related text data; and outputting the oral healthcare information parsing results to users through the information interaction platform to assist users in understanding their own oral healthcare status.

[0010] Specifically, S5 involves: storing user consultation data, oral medical consultation response information, and oral medical information analysis results to form a user oral medical information interaction history; analyzing the user's oral medical consultation behavior and the types of oral medical information they are interested in based on the user's oral medical information interaction history; and adjusting and optimizing the consultation response content and oral medical service guidance methods in the subsequent oral medical information interaction process executed by the oral specialty AI agent based on the analysis results.

[0011] An AI-based oral healthcare information interaction system includes: The dental specialty AI agent building unit is used to acquire local dental specialty knowledge data corresponding to at least one dentist, and to fuse the local dental specialty knowledge data with a general artificial intelligence language model to generate a dental specialty AI agent for information interaction in dental medical scenarios. The information interaction carrier construction unit is used to build an information interaction carrier based on the dental AI intelligent agent, and bind dental medical information display content in the information interaction carrier, so that users can access dental medical information and initiate dental medical consultation requests through the information interaction carrier. The consultation request receiving and parsing unit is used to receive oral medical consultation requests initiated by users through an information interaction carrier, and call the oral specialty AI agent to perform semantic parsing on the oral medical consultation requests and generate oral medical semantic parsing results. The consultation response generation and output unit is used to generate corresponding oral medical consultation response information based on the results of oral medical semantic parsing, and output the oral medical consultation response information to the user through the information interaction carrier, while also outputting oral medical service guidance information associated with the oral medical consultation request. The oral healthcare information self-service analysis unit is used to receive oral healthcare-related data submitted by users through the information interaction medium, call the oral healthcare AI agent to analyze the oral healthcare-related data, generate oral healthcare information analysis results corresponding to the oral healthcare-related data, and output the oral healthcare information analysis results to users through the information interaction medium; The interactive data storage and analysis unit is used to store oral medical consultation requests, oral medical consultation response information, and oral medical information parsing results generated by users during oral medical information interaction, forming a user oral medical information interaction history record, and analyzing the user oral medical information interaction history record to generate analysis results. The interaction process optimization unit is used to adjust and optimize the oral medical consultation response information and the output oral medical service guidance information generated in the subsequent oral medical information interaction process based on the analysis results, so as to achieve continuous optimization of the oral medical information interaction process.

[0012] The beneficial effects of this invention are as follows: This invention integrates local dental specialty knowledge data from dentists with a general artificial intelligence language model to construct a dental specialty AI agent. This ensures that the generated consultation responses maintain natural language interaction capabilities while being constrained by the boundaries of dental specialty knowledge, avoiding the problems of insufficient professionalism or inconsistent responses found in general intelligent question-and-answer systems, thus improving the professionalism and stability of dental medical information interaction. By constructing an information interaction carrier based on the dental specialty AI agent and integrating dental medical information display and consultation interaction entry points within the same carrier, users can complete information browsing, consultation initiation, and result acquisition through a single interaction entry point, lowering the user threshold and improving the convenience of accessing dental medical information.

[0013] This invention performs semantic parsing on users' oral healthcare consultation requests and generates corresponding oral healthcare consultation response information based on the parsing results. Simultaneously, it outputs oral healthcare service guidance information associated with the consultation content, giving the consultation response process a clear processing logic and output rules, reducing manual intervention, and improving the efficiency of oral healthcare consultation response. By receiving and parsing dental image data, oral medication-related image data, or oral diagnosis-related text data, it generates corresponding oral healthcare information parsing results, allowing patients to obtain understandable explanations of relevant information without relying on manual interpretation, thus solving the problem of fragmented multi-source data parsing capabilities in existing technologies.

[0014] This invention creates a unified storage system for consultation requests, responses, and analysis results, forming a historical record of user oral healthcare information interactions. This ensures the traceability of the doctor-patient interaction process and provides a reliable data foundation for subsequent analysis of user consultation behavior characteristics and information types of interest. By analyzing this historical record and adjusting and optimizing subsequent consultation responses and oral healthcare service guidance methods based on the analysis results, the oral healthcare information interaction process can be continuously iterated and improved based on real-world usage data, overcoming the lack of a closed-loop optimization mechanism in existing technologies. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an oral medical information interaction method based on an AI agent according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: As Figure 1 As shown, this embodiment provides a method for oral medical information interaction based on an AI agent, including the following steps: S1. Steps for constructing an AI agent for oral medicine: The local oral medicine knowledge base corresponding to the dentist is fused with a general artificial intelligence language model. Based on the fusion result, an oral medicine AI agent for information interaction in oral medical scenarios is generated. The local oral medicine knowledge base includes at least oral disease diagnosis and treatment guidelines, oral medication instructions, oral treatment item descriptions, and doctor practice-related information. S2, Step of generating a socialized information interaction carrier: The dental AI agent generated in step S1 is encapsulated into an information interaction carrier that can be accessed and forwarded by users on a social platform, so that the information interaction carrier has both the ability to display dental medical information and the information interaction entry based on the dental AI agent. S3. Perform oral medical consultation interaction steps: Receive oral medical consultation requests initiated by users through the information interaction carrier, call the oral specialty AI agent to parse the oral medical consultation requests and generate corresponding oral medical consultation response information, and synchronously output oral medical service guidance information related to the consultation content in the oral medical consultation response information. S4. Perform the self-service parsing step of oral medical information: Based on step S3, receive oral medical related data submitted by the user through the information interaction carrier, and parse the oral medical related data based on the oral specialty AI agent to generate oral medical information parsing results corresponding to the oral medical related data. The oral medical related data includes at least tooth image data, oral medication related image data or oral diagnosis related text data. S5. Storing and utilizing the traceability optimization steps of interactive data: Store the user consultation data, interactive response data and oral medical information parsing results generated in steps S3 and S4 to form a user oral medical information interaction history record, and adjust and optimize the response content and service guidance methods in subsequent oral medical information interaction processes based on the user oral medical information interaction history record.

[0018] S1 specifically includes the following sub-steps: S110, Local Oral Specialty Knowledge Data Acquisition Steps: Acquire local oral specialty knowledge data corresponding to at least one dentist, and organize the local oral specialty knowledge data into a structured or semi-structured data set that can be processed subsequently; the local oral specialty knowledge data includes at least: oral disease diagnosis and treatment guidelines data, oral medication instructions data, oral treatment item instructions data, and doctor practice-related information data.

[0019] To facilitate subsequent fusion processing, the local dental specialty knowledge data will be denoted as... .

[0020] in, This represents local dental specialty knowledge data (a collection of data used to characterize the local specialty knowledge content of dentists).

[0021] S120, Knowledge Fusion Processing Step: The local dental specialty knowledge data obtained in step S110 is processed... The language is fused with a general artificial intelligence language model to obtain a fused semantic representation for oral healthcare scenarios, and the general artificial intelligence language model is equipped with the ability to understand and generate oral specialty semantics while retaining the ability to interact with natural language.

[0022] Specifically, based on Generate local specialty semantic representations And a general semantic representation is generated by a general artificial intelligence language model for the same input. ,right and Weighted fusion is performed to obtain the fused semantic representation. The calculation method is as follows: in: This represents the semantic representation of local specialty (based on local dental specialty knowledge data). The converted data is used to carry semantic information related to dental specialty knowledge. It represents a general semantic representation (generated by a general artificial intelligence language model, used to carry general language understanding and semantic information generation). The fusion semantic representation (by) and The fusion is used to drive semantic understanding and response generation in oral healthcare scenarios. This represents the weighting coefficient for specialization integration (used to control the local specialization semantic representation). In fusion semantic representation (the proportion in), and satisfying: Based on this, semantic representation will be integrated. As a constraint or guiding information, it acts on the output process of the general artificial intelligence language model, so that its output content conforms to the boundaries of specialized knowledge and expression habits in the field of oral medicine.

[0023] In specific implementation, the fusion processing in step S120 adopts a Retrieval Augmentation (RAG) architecture. First, local dental specialty knowledge data is vectorized and encoded using a text embedding model (such as BERT or Text2Vec), and the encoded vectors are stored in a vector database as the local specialty semantic representation K. When the general artificial intelligence language model receives input information, the system transforms the input information into a query vector and retrieves the Top-N most similar specialty knowledge fragments from the vector database. Subsequently, the retrieved specialty knowledge fragments are used as background context and concatenated with the input information according to a preset prompt word template to construct an input sequence containing specialty constraints. Finally, this input sequence is input into the general artificial intelligence language model, forcing the model to generate responses based on the retrieved local knowledge, thereby technically achieving the fusion of local specialty semantics and general semantics.

[0024] S130, Generation steps of the dental specialty AI agent: Based on the fused semantic representation obtained in step S120 This generates a dental AI agent for oral medical information interaction, and enables the dental AI agent to perform semantic understanding, information retrieval / association, and response generation for oral medical consultation requests.

[0025] The aforementioned dental AI intelligent entity is denoted as... And enable it to receive oral medical consultation requests Output oral medical consultation response information in real time The correspondence satisfies: in: Represents an AI agent for oral specialty (based on fusion semantic representation) (Build, for performing information interaction tasks in oral healthcare scenarios). This indicates a request for oral healthcare consultation (consultation content related to oral healthcare entered by the user). This indicates the oral healthcare consultation response information (generated by an AI agent specializing in oral health). Requests for oral healthcare consultation The output response content.

[0026] S2 specifically includes the following sub-steps: S210, Information Interaction Carrier Construction Steps: Based on the dental AI intelligent agent generated in step S130 Constructing a dental specialty AI agent to support the oral cavity specialty. The information interaction carrier, and enables the information interaction carrier to have the ability to interact with dental AI intelligent agents. Its interactive calling capabilities.

[0027] Let the information interaction carrier be denoted as .

[0028] in: This represents an information interaction carrier (used to carry and provide dental AI intelligent agents to external parties). (The carrier of information interaction entry point).

[0029] S220, Steps for Binding Oral Medical Information Display: On the information interaction platform The content is integrated with oral healthcare information display to enable users to access information interaction platforms. The system allows users to access information related to oral healthcare and enter interactive portals. The oral healthcare information displayed includes at least information on oral healthcare services, doctor's practice information, and oral healthcare consultation portal information.

[0030] The content of the oral medical information display is denoted as .

[0031] in: This indicates the content displayed for oral healthcare (used in information interaction media). This is a collection of content displayed to users, showcasing oral healthcare services and information related to doctors' practice.

[0032] S230, Interactive Entry Opening and Request Mapping Steps: Enable Information Interaction Carriers By opening up access to users through social platforms, users can interact with information through these platforms. Initiate a dental health consultation request When the oral medical consultation request is received At that time, information exchange carrier The oral medical consultation request is mapped according to the preset interaction request mapping rules. Converted into standardized oral healthcare interactive input and input the standardized oral medical interaction. Submitted to the dental AI intelligent system For processing, the mapping relationship satisfies: in: This represents standardized oral healthcare interactive input (from information interaction carriers). Request for oral medical consultation The standardized version is used for dental AI intelligent systems. (The input content to be parsed and processed). This indicates the interaction request mapping rules (used to map oral medical consultation requests). Converted into standardized oral healthcare interactive input (The set of rules).

[0033] S3 specifically includes the following sub-steps: S310, Steps for Receiving Oral Medical Consultation Requests: Access to the Information Interaction Platform via Step S230 Receive oral healthcare consultation requests initiated by users The oral medical consultation request mentioned above This refers to the oral healthcare-related consultation content entered by the user in text format.

[0034] Upon receiving the oral medical consultation request Afterwards, information exchange carrier The oral medical consultation request will be mapped according to the interaction request mapping rules in step S230. Converted into standardized oral healthcare interactive input For subsequent processing.

[0035] S320, Consultation Request Parsing Steps: Transform the standardized oral healthcare interaction input obtained in step S310... Submitted to the dental AI intelligent system AI intelligent agent for dental specialties Standardized oral healthcare interactive input Perform semantic parsing to generate oral medical semantic parsing results. .

[0036] in, This represents the semantic analysis results of oral medical treatment (generated by an AI agent specializing in oral medicine). Standardized oral healthcare interactive input The analysis results are used to characterize the user's consultation focus and the semantic points of oral medical care.

[0037] S330, Consultation Response Generation and Output Steps: Based on the oral medical semantic parsing results generated in step S320. AI intelligent agent for dental specialties Generate oral medical consultation response information based on preset response generation rules. and through information exchange carriers Output the oral medical consultation response information to the user The oral medical consultation response information mentioned therein Semantic analysis results of oral medicine The following correspondence is satisfied: in: This indicates the oral healthcare consultation response information (generated by an AI agent specializing in oral health). (The response content generated and output based on the user's oral medical consultation). This indicates the response generation rules (used based on the results of oral medical semantic parsing). Generate matching oral healthcare consultation response information (The set of rules).

[0038] Furthermore, information interaction carriers Based on the oral medical semantic analysis results Information on oral healthcare consultation responses Match, generate, and output oral healthcare service guidance information. To guide users to obtain the oral healthcare consultation request. Related oral healthcare services.

[0039] in, This indicates guidance information for oral healthcare services (and the results of semantic analysis of oral healthcare). and oral medical consultation response information Related, guiding content used to direct users through subsequent oral healthcare service processes.

[0040] S4 specifically includes the following sub-steps: S410, Steps for Receiving Oral Medical Data: After completing the oral medical consultation response output in step S330, the data is received via an information exchange carrier. Receive oral healthcare-related data submitted by users and identify the type of the oral healthcare-related data for subsequent parsing and processing.

[0041] Record the oral healthcare-related data submitted by users as .

[0042] in, This represents oral healthcare-related data (obtained by users through information exchange platforms). The submitted data set is used for self-service parsing of oral medical information, and the oral medical related data includes at least one of dental image data, oral medication related image data, or oral diagnosis related text data.

[0043] S420, Oral healthcare-related data parsing steps: Parse the oral healthcare-related data received in step S410. Submitted to the dental AI intelligent system AI intelligent agent for dental specialties The oral healthcare-related data is processed according to preset oral healthcare information parsing rules. The data is analyzed and processed to generate data related to the oral medical care. Corresponding oral medical information analysis results The correspondence satisfies: in: This indicates the results of the oral medical information analysis (by an oral specialty AI agent). Data related to oral healthcare The data obtained through analysis is used to characterize the oral healthcare-related data. (corresponding output of oral medical information) Rules for parsing oral healthcare information (used for parsing oral healthcare-related data) Perform parsing and generate oral medical information parsing results. (The set of rules).

[0044] Furthermore, the results of the oral medical information analysis It should include at least one or more of the following: symptom alerts, medication alerts, treatment recommendations, or report interpretation alerts.

[0045] For the dental image data included in the oral medical data, the parsing process in step S420 specifically includes the following computer vision processing flow: First, a pre-trained target detection model (such as YOLO series or Faster R-CNN) is called to extract the region of interest from the dental image, identify and crop out individual teeth, gingival margins, or lesion areas; second, the cropped image region is input into a visual encoder (such as ResNet or ViT) to extract the deep visual feature vector of the image; finally, multimodal alignment technology is used to map the visual feature vector to a vector space with the same semantic meaning as the text, or the visual features are converted into corresponding text description labels (such as "enamel defects" or "gingival redness and swelling"), and these features or labels are input into the AI ​​agent, combined with oral diagnosis-related text data for comprehensive reasoning, to generate the oral medical information parsing result.

[0046] S430, Steps for outputting parsing results: via information interaction carrier Output the oral medical information parsing results generated in step S420 to the user So that users can analyze the oral medical information based on the results. Obtain the oral medical-related data The information is explained and used to help users understand their oral health condition or precautions related to medication / treatment.

[0047] S5 specifically includes the following sub-steps: S510, Interactive Data Storage Step: Data storage of oral medical consultation requests generated during steps S310 to S430. Oral medical consultation response information and oral healthcare information analysis results Store the data to create a historical record of user oral healthcare information interactions. The set relation satisfies: in, This indicates the user's oral healthcare information interaction history (from oral healthcare consultation requests). Oral medical consultation response information and oral medical information analysis results (A collection of historical data used to record the process of users' oral medical information interaction).

[0048] S520, Historical Record Analysis Steps: Based on the user oral healthcare information interaction history formed in step S510 The user's oral medical information interaction history is analyzed according to preset data analysis rules. Perform analysis and processing to generate analysis results. The correspondence satisfies: in: This indicates the results of the historical record analysis (based on the user's oral healthcare information interaction history). The analysis results were obtained to characterize user consultation behavior and the types of information they focus on. This indicates data analysis rules (used for analyzing users' oral healthcare information interaction history). Perform analysis and output historical data analysis results. (The set of rules).

[0049] S530, Interaction process optimization steps: Based on the historical record analysis results of step S520 Oral medical consultation response information generated during subsequent oral medical information exchange. And the output of oral healthcare service guidance information Adjustments and optimizations will be made to ensure that subsequent output content matches users' oral healthcare consultation preferences and the types of information they are interested in.

[0050] Let the adjusted and optimized output strategy be denoted as... And the output strategy Results of historical record analysis The following correspondence is satisfied: in: This indicates the output strategy (used to constrain oral healthcare consultation response information during subsequent oral healthcare information interaction). Information on guidance for oral healthcare services (a set of strategies for generation and output methods). This indicates the policy update rules (used to analyze results based on historical records). Generate or update output strategy (The set of rules).

[0051] The optimization of the interaction process is achieved through dynamic updates to the knowledge base and iterative updates to the prompt word strategy. Specifically, the system automatically identifies interaction records with negative user feedback (such as repeated questions or marking as unsatisfactory) and marks them as negative samples. The system extracts high-frequency keywords from the negative samples. If the retrieval matching degree of such keywords in the local dental specialty knowledge base is lower than a preset threshold, a knowledge gap alarm is triggered. Relevant knowledge entries are then added manually or automatically, and the vector database index is updated, thereby expanding the local specialty semantic representation K. Simultaneously, the system extracts high-quality question-answer pairs with high user ratings and adds them to the few-sample learning example library of prompt words. These serve as reference examples when generating responses in subsequent interactions, thus enabling dynamic adjustment of the output strategy U.

[0052] Example 2: This example provides an oral medical information interaction system based on an AI agent, including: The dental specialty AI agent construction unit is used to acquire local dental specialty knowledge data corresponding to at least one dentist, and to fuse the local dental specialty knowledge data with a general artificial intelligence language model to generate a dental specialty AI agent for information interaction in dental medical scenarios. The information interaction carrier construction unit is used to construct an information interaction carrier based on the oral specialty AI intelligent agent, and bind oral medical information display content in the information interaction carrier so that users can access oral medical information and initiate oral medical consultation requests through the information interaction carrier. The consultation request receiving and parsing unit is used to receive oral medical consultation requests initiated by users through the information interaction carrier, and call the oral specialty AI agent to perform semantic parsing on the oral medical consultation request and generate oral medical semantic parsing results. The consultation response generation and output unit is used to generate corresponding oral medical consultation response information based on the oral medical semantic parsing results, and output the oral medical consultation response information to the user through the information interaction carrier, while also outputting oral medical service guidance information associated with the oral medical consultation request; The oral medical information self-service parsing unit is used to receive oral medical-related data submitted by users through the information interaction carrier, call the oral specialty AI agent to parse the oral medical-related data, generate oral medical information parsing results corresponding to the oral medical-related data, and output the oral medical information parsing results to users through the information interaction carrier; The interactive data storage and analysis unit is used to store oral medical consultation requests, oral medical consultation response information, and oral medical information parsing results generated by users during oral medical information interaction, forming a user oral medical information interaction history record, and to analyze the user oral medical information interaction history record to generate analysis results. The interaction process optimization unit is used to adjust and optimize the oral medical consultation response information generated and the oral medical service guidance information output during the subsequent oral medical information interaction process based on the analysis results, so as to achieve continuous optimization of the oral medical information interaction process.

[0053] The system described in this embodiment is deployed in a high-performance computing server cluster, and the server is equipped with GPU (Graphics Processing Unit) computing units for accelerating deep learning inference. The local dental specialty knowledge base is stored in a distributed database or vector retrieval system. The information interaction carrier establishes a connection with the server through encrypted network communication protocols (such as HTTPS or WebSocket) to realize real-time transmission and interaction of image data and text streams.

[0054] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0055] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0056] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0059] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0061] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0063] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for oral medical information interaction based on AI intelligent agents, characterized in that, Includes the following steps: S1. The local oral specialty knowledge base corresponding to the dentist is fused with the general artificial intelligence language model, and an oral specialty AI agent for information interaction in oral medical scenarios is generated based on the fusion result. S2. Encapsulate the dental AI agent into an information interaction carrier that can be accessed and forwarded by users on social platforms, so that the information interaction carrier has both the ability to display dental medical information and the information interaction entry point based on the dental AI agent. S3. Receive oral medical consultation requests initiated by users through information interaction carriers, call oral specialist AI intelligent agents to parse the oral medical consultation requests and generate corresponding oral medical consultation response information, and synchronously output oral medical service guidance information related to the consultation content in the oral medical consultation response information. S4. Receive oral healthcare-related data submitted by users through information interaction carriers, and parse the oral healthcare-related data based on the oral healthcare AI agent to generate oral healthcare information parsing results corresponding to the oral healthcare-related data.

2. The oral medical information interaction method based on AI intelligent agent according to claim 1, characterized in that, It also includes S5, which stores user consultation data, interaction response data, and oral medical information analysis results to form a user oral medical information interaction history record, and adjusts and optimizes the response content and service guidance methods in subsequent oral medical information interaction processes based on the user oral medical information interaction history record.

3. The oral medical information interaction method based on AI intelligent agent according to claim 1, characterized in that, S1 specifically refers to: Acquire local oral specialty knowledge data corresponding to at least one dentist. Local oral specialty knowledge data includes oral disease diagnosis and treatment guidelines, oral medication instructions, oral treatment procedure instructions, and physician practice-related information. Local oral specialty knowledge data is input into a general artificial intelligence language model for fusion processing, so that the general artificial intelligence language model has the ability to understand the specialty semantics of oral medicine while retaining the ability to interact with natural language. Based on the fusion processing results, a dental specialty AI agent is generated for oral medical information interaction. This dental specialty AI agent is used for consultation analysis and information output in the subsequent oral medical information interaction process.

4. The oral medical information interaction method based on AI intelligent agent according to claim 1, characterized in that, S2 specifically refers to: Based on the dental AI agent, an information interaction carrier is constructed to carry the dental AI agent, enabling the information interaction carrier to call the dental AI agent. The oral medical information display content corresponding to the oral specialty AI intelligent body is bound to the information interaction carrier. The oral medical information display content includes oral medical service description information, doctor practice information and oral medical consultation portal information. This enables information exchange platforms to be made accessible to users, allowing users to initiate oral healthcare information exchange requests through these platforms.

5. The oral medical information interaction method based on AI intelligent agent according to claim 1, characterized in that, S3 specifically refers to: The system receives oral healthcare consultation requests initiated by users through an information exchange medium. These requests are entered in text format. The dental AI agent is invoked to perform semantic parsing on dental medical consultation requests and determine the dental medical consultation content corresponding to the dental medical consultation requests. Based on the analysis results, corresponding oral medical consultation response information is generated and output to the user through an information interaction medium. At the same time, oral medical service guidance information related to the consultation content is also output.

6. The oral medical information interaction method based on AI intelligent agent according to claim 1, characterized in that, S4 specifically refers to: Receive oral healthcare-related data submitted by users through information exchange platforms; The system calls upon an AI agent specializing in oral health to analyze oral healthcare-related data and generate oral healthcare information analysis results corresponding to the data. The oral healthcare-related data includes tooth image data, oral medication-related image data, or oral diagnosis-related text data. The system outputs oral healthcare information analysis results to users through information interaction carriers, which helps users understand their own oral healthcare status.

7. The oral medical information interaction method based on AI intelligent agent according to claim 2, characterized in that, S5 specifically refers to: The system stores user consultation data, oral medical consultation response information, and oral medical information analysis results to form a user oral medical information interaction history. Based on users' oral healthcare information interaction history, we analyze users' oral healthcare consultation behavior and the types of oral healthcare information they are interested in.

8. The oral medical information interaction method based on AI intelligent agent according to claim 7, characterized in that, S5 also includes: adjusting and optimizing the consultation and response content and the guidance method for oral medical services in the subsequent oral medical information interaction process executed by the oral specialty AI agent, based on the analysis results.

9. An AI-based oral medical information interaction system, based on the AI-based oral medical information interaction method according to any one of claims 1-8, characterized in that, include: The dental specialty AI agent construction unit is used to acquire local dental specialty knowledge data corresponding to at least one dentist, integrate the local dental specialty knowledge data with a general artificial intelligence language model, and generate a dental specialty AI agent for information interaction in dental medical scenarios. The information interaction carrier construction unit is used to build an information interaction carrier based on the dental AI intelligent agent, and bind dental medical information display content in the information interaction carrier, so that users can access dental medical information and initiate dental medical consultation requests through the information interaction carrier. The consultation request receiving and parsing unit is used to receive oral medical consultation requests initiated by users through an information interaction carrier, and call the oral specialty AI agent to perform semantic parsing on the oral medical consultation requests and generate oral medical semantic parsing results. The consultation response generation and output unit is used to generate corresponding oral medical consultation response information based on the results of oral medical semantic parsing, and output the oral medical consultation response information to the user through the information interaction carrier, while also outputting oral medical service guidance information associated with the oral medical consultation request. The oral healthcare information self-service analysis unit is used to receive oral healthcare-related data submitted by users through the information interaction medium, call the oral healthcare AI agent to analyze the oral healthcare-related data, generate oral healthcare information analysis results corresponding to the oral healthcare-related data, and output the oral healthcare information analysis results to users through the information interaction medium; The interactive data storage and analysis unit is used to store oral medical consultation requests, oral medical consultation response information, and oral medical information parsing results generated by users during oral medical information interaction, forming a user oral medical information interaction history record, and to analyze the user oral medical information interaction history record to generate analysis results. The interaction process optimization unit is used to adjust and optimize the oral medical consultation response information and the output oral medical service guidance information generated in the subsequent oral medical information interaction process based on the analysis results, so as to achieve continuous optimization of the oral medical information interaction process.

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