OBJECTIVE To develop an artificial intelligence-based intelligent auxiliary diagnosis and treatment system for Xin'an medicine to address the challenges of integrating ancient Xin'an medical case records into modern clinical applications.

METHODS The project involved structuring and standardizing case records from ancient texts of Xin'an medicine to build a comprehensive Xin'an medicine database. Advanced techniques, such as data annotation, entity relationship extraction, and data mining, were applied to create a Xin'an medicine knowledge base. Furthermore, a knowledge graph of Xin'an medicine was constructed using techniques for knowledge acquisition, integration, storage, and graph-based question-answering, improving the efficiency of knowledge organization and retrieval. The LangChain framework was utilized to connect the Xin'an medicine knowledge base to a large language model, enabling a model-driven local knowledge base question-answering system.

RESULTS The study successfully established a systematic and standardized knowledge base for Xin'an medical case records. The application of knowledge graph technology provided a clear visualization of Xin'an medicine's knowledge structure, and the development of an intelligent question-answering module significantly improved the efficiency of knowledge management and retrieval. The local knowledge base question-answering system, powered by a large language model and based on Xin'an medicine's theoretical and practical expertise, delivered accurate diagnostic and treatment support, promoting the heritage and innovation of Xin'an medicine.

CONCLUSION This research validates the feasibility of modernizing traditional medical texts and provides an innovative approach to knowledge development and clinical application in Chinese medicine. The findings have significant academic value and promising clinical implications.