Ren Yaxuan, Luo Xufei, Wang Ye, Li Haodong, Zhang Hairong, Li Zeming, Lai Honghao, Li Xuanlin, Ge Long, Estill Janne, Zhang Lu, Yang Shu, Chen Yaolong, Wen Chengping, Bian Zhaoxiang
School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
J Evid Based Med. 2025 Mar;18(1):e12658. doi: 10.1111/jebm.12658. Epub 2024 Dec 9.
The application of large language models (LLMs) in medicine has received increasing attention, showing significant potential in teaching, research, and clinical practice, especially in knowledge extraction, management, and understanding. However, the use of LLMs in Traditional Chinese Medicine (TCM) has not been thoroughly studied. This study aims to provide a comprehensive overview of the status and challenges of LLM applications in TCM.
A systematic search of five electronic databases and Google Scholar was conducted between November 2022 and April 2024, using the Arksey and O'Malley five-stage framework to identify relevant studies. Data from eligible studies were comprehensively extracted and organized to describe LLM applications in TCM and assess their performance accuracy.
A total of 29 studies were identified: 24 peer-reviewed articles, 1 review, and 4 preprints. Two core application areas were found: the extraction, management, and understanding of TCM knowledge, and assisted diagnosis and treatment. LLMs developed specifically for TCM achieved 70% accuracy in the TCM Practitioner Exam, while general-purpose Chinese LLMs achieved 60% accuracy. Common international LLMs did not pass the exam. Models like EpidemicCHAT and MedChatZH, trained on customized TCM corpora, outperformed general LLMs in TCM consultation.
Despite their potential, LLMs in TCM face challenges such as data quality and security issues, the specificity and complexity of TCM data, and the nonquantitative nature of TCM diagnosis and treatment. Future efforts should focus on interdisciplinary talent cultivation, enhanced data standardization and protection, and exploring LLM potential in multimodal interaction and intelligent diagnosis and treatment.
大语言模型（LLMs）在医学领域的应用受到越来越多的关注，在教学、研究和临床实践中显示出巨大潜力，尤其是在知识提取、管理和理解方面。然而，大语言模型在中医（TCM）中的应用尚未得到充分研究。本研究旨在全面概述大语言模型在中医应用中的现状和挑战。
于2022年11月至2024年4月期间，使用阿克西和奥马利的五阶段框架对五个电子数据库和谷歌学术进行系统检索，以识别相关研究。对符合条件的研究数据进行全面提取和整理，以描述大语言模型在中医中的应用并评估其性能准确性。
共识别出29项研究：24篇同行评审文章、1篇综述和4篇预印本。发现了两个核心应用领域：中医知识的提取、管理和理解，以及辅助诊断和治疗。专门为中医开发的大语言模型在中医师考试中准确率达到70%，而通用中文大语言模型准确率为60%。常见的国际大语言模型未通过该考试。像EpidemicCHAT和MedChatZH这样在定制的中医语料库上训练的模型，在中医咨询方面优于通用大语言模型。
尽管大语言模型在中医领域具有潜力，但仍面临数据质量和安全问题、中医数据的特异性和复杂性以及中医诊断和治疗的非量化性质等挑战。未来的努力应集中在跨学科人才培养、加强数据标准化和保护，以及探索大语言模型在多模态交互和智能诊断治疗方面的潜力。
