摘要
中医辨证论治是传统医学体系的核心,几千年来为中华民族的健康发展发挥着不可或缺的作用。近年来,在多部门的协同推进下,我国中医科技创新能力持续提升。中医辨证与人工智能、大数据等多学科交叉融合,取得新的进展,工程化前沿方法和技术为突破中医辨证理论瓶颈提供了有效路径。本文以新时代中医诊断现代化、智能化发展为背景,总结中医辨证的基本原理,概括人工智能辅助中医辨证的基本过程以及多模态数据融合、症状关联性分析、证候量化、证候推理、中医药大模型等关键问题,总结了每个环节的研究思路和发展现状,进而根据现有不足分析当前发展面临的可利用公开数据较少且质量参差不齐,辨证模型单一、普适性不佳,辨证模型的可解释性不足且存在差异,辨证模型结果评价存在局限、缺乏可信度等挑战。研究建议,加强数据整合与质量把控,深度融合人工智能技术与中医辨证思维、加强模型可解释性,发展中医药细分领域的大语言模型,加强智能中医人才队伍建设、鼓励多领域专家合作,完善国际标准和法律法规、加强国际合作与交流等,以期为智能中医诊疗的技术探索和科技创新提供参考。
Traditional Chinese medicine(TCM)syndrome differentiation,as the core of the traditional Chinese medical system,has played an indispensable role in guaranteeing the health of the Chinese nation for thousands of years.Recently,with the collaborative promotion of multiple departments,the TCM technology innovation capability of China has been continuously enhanced.The integration of TCM syndrome differentiation with artificial intelligence AI,big data,and other fields has made new progress.Engineering frontier methods and technologies have provided an effective route for breaking through the theoretical bottlenecks of TCM syndrome differentiation.Against the backdrop of the modernization and intelligent development of TCM diagnosis in the new era,this study summarizes the fundamental theories,basic processes,and key technical links of AI-assisted TCM syndrome differentiation.The key technical links include multimodal data fusion,symptom correlation analysis,syndrome quantification,syndrome reasoning,and large-scale TCM models.It also expounds on the research ideas and development status of each link and summarizes the challenges faced by AI-assisted TCM syndrome differentiation.For instance,publicly available data are insufficient and have a poor quality;syndrome differentiation models are inadequate,have poor universality,and lack interpretability and consistency;and the evaluation of syndrome differentiation model results is limited and lack credibility.Therefore,the following suggestions are proposed for reference:(1)strengthening data integration and quality control;(2)deeply integrating AI with TCM syndrome differentiation to enhance model interpretability;(3)developing large language models for the subdivisions of TCM;(4)strengthening the construction of intelligent TCM talent teams and encouraging cooperation among experts in multiple fields;and(5)improving international standards and regulations to strengthen international cooperation and exchanges.These efforts aim to provide references for the technological exploration and innovation of AI-assisted TCM diagnosis and treatment.
作者
宋逸杰
马素亚
戴亚盛
陆军
Song Yijie;Ma Suya;Dai Yasheng;Lu Jun(School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China;Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province,Jiaxing 314001,Zhejiang,China;Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems,Jiaxing 314001,Zhejiang,China;Guang’anmen Hospital,China Academy of Chinese Medical Sciences,Beijing 100053,China)
出处
《中国工程科学》
CSCD
北大核心
2024年第2期234-244,共11页
Strategic Study of CAE
基金
中国工程院咨询项目“面向中医药的人工智能发展战略研究”(2023-HY-10)
浙江省“鲲鹏行动”计划。
关键词
中医药
中医诊断
人工智能
智能辨证
中医智能化
traditional Chinese medicine
TCM diagnosis
artificial intelligence
intelligent syndrome differentiation
intelligent development of TCM
