Large Language Models in Traditional Chinese Medicine: A Scoping Review

Yaxuan Ren; Xufei Luo; Ye Wang; Haodong Li; Hairong Zhang; Zeming Li; Honghao Lai; Xuanlin Li; Long Ge; Janne ESTILL; Lu Zhang; Shu Yang; Yaolong Chen; Chengping Wen; Zhaoxiang Bian

doi:10.1111/jebm.12658

Journal of Evidence-Based Medicine ›› 2025, Vol. 18 ›› Issue (1) : e12658 DOI: 10.1111/jebm.12658

REVIEW

Large Language Models in Traditional Chinese Medicine: A Scoping Review

Yaxuan Ren ¹
, Xufei Luo ²^,³^,⁴^,⁵
, Ye Wang ⁶
, Haodong Li ⁶
, Hairong Zhang ²^,³
, Zeming Li ⁷
, Honghao Lai ⁸^,⁹
, Xuanlin Li ¹⁰^,¹¹
, Long Ge ⁸^,⁹
, Janne ESTILL ²^,¹²
, Lu Zhang ⁷
, Shu Yang ¹^,^†
, Yaolong Chen ²^,³^,⁴^,⁵^,⁶
, Chengping Wen ¹⁰^,¹¹
, Zhaoxiang Bian ¹³

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¹. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China

². Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

³. Key Laboratory of Evidence-Based Medicine of Gansu Province, Lanzhou, China

⁴. Institute of Health Data Science, Lanzhou University, Lanzhou, China

⁵. WHO Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China

⁶. School of Public Health, Lanzhou University, Lanzhou, China

⁷. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

⁸. Department of Health Policy and Health Management, School of Public Health, Lanzhou University, Lanzhou, China

⁹. Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China

¹⁰. College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China

¹¹. Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, Hangzhou, China

¹². Institute of Global Health, University of Geneva, Geneva, Switzerland

¹³. School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China

yangshu@cdutcm.edu.cn

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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords

Chinese Herbal Medicine / Large language model / Scoping review / Traditional Chinese Medicine

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Yaxuan Ren, Xufei Luo, Ye Wang, Haodong Li, Hairong Zhang, Zeming Li, Honghao Lai, Xuanlin Li, Long Ge, Janne ESTILL, Lu Zhang, Shu Yang, Yaolong Chen, Chengping Wen, Zhaoxiang Bian. Large Language Models in Traditional Chinese Medicine: A Scoping Review. Journal of Evidence-Based Medicine, 2025, 18(1): e12658 DOI:10.1111/jebm.12658