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Large language models in traditional Chinese medicine: a systematic review
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Zhe Chen1, 2, 3, Hui Wang1, Chengxian Li1, 2, Chunxiang Liu1, Fengwen Yang1, Dong Zhang1, Alice Josephine Fauci4, 5, *, Junhua Zhang1, 2, *
Acupuncture and Herbal Medicine | 2025, 5(1) : 57 - 67
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Acupuncture and Herbal Medicine | 2025, 5(1): 57-67
• Review Articles •
Large language models in traditional Chinese medicine: a systematic review
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Zhe Chen1, 2, 3, Hui Wang1, Chengxian Li1, 2, Chunxiang Liu1, Fengwen Yang1, Dong Zhang1, Alice Josephine Fauci4, 5, *, Junhua Zhang1, 2, *
Affiliations
  • 1Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
  • 2Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
  • 3National Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, China
  • 4Italian National Institute of Health, Rome, Italy
  • 5Istituto Superiore di Sanità, Centro Eccellenza Clinica, Qualità e Sicurezza delle Cure, Rome, Italy
Published: 2025-03-25 doi: 10.1097/HM9.0000000000000143
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Objective:

Generative artificial intelligence (AI) technology, represented by large language models (LLMs), has gradually been developed for traditional Chinese medicine (TCM); however, challenges remain in effectively enhancing AI applications for TCM. Therefore, this study is the first systematic review to analyze LLMs in TCM retrospectively, focusing on and summarizing the evidence of their performance in generative tasks.

Methods:

We extensively searched electronic databases for articles published until June 2024 to identify publicly available studies on LLMs in TCM. Two investigators independently selected and extracted the related information and evaluation metrics. Based on the available data, this study used descriptive analysis for a comprehensive systematic review of LLM technology related to TCM.

Results:

Ten studies published between 2023 and 2024 met our eligibility criteria and were included in this review, including 40% LLMs in the TCM vertical domain, 40% containing TCM data, and 20% honoring the TCM contribution, with a foundational model parameter range from 1.8 to 33 billion. All included studies used manual or automatic evaluation metrics to evaluate model performance and fully discussed the challenges and contributions through an overview of LLMs in TCM.

Conclusions:

LLMs have achieved significant advantages in TCM applications and can effectively address intelligent TCM tasks. Further in-depth development of LLMs is needed in various vertical TCM fields, including clinical and fundamental research. Focusing on the functional segmentation development direction of generative AI technologies in TCM application scenarios to meet the practical needs-oriented demands of TCM digitalization is essential.

Generative artificial intelligence  /  Intelligence clinical applications  /  Large language model  /  Systematic review  /  Traditional Chinese medicine
Zhe Chen, Hui Wang, Chengxian Li, Chunxiang Liu, Fengwen Yang, Dong Zhang, Alice Josephine Fauci, Junhua Zhang. Large language models in traditional Chinese medicine: a systematic review[J]. Acupuncture and Herbal Medicine, 2025 , 5 (1) : 57 -67 . DOI: 10.1097/HM9.0000000000000143
  • National Multidisciplinary Innovation Team of Traditional Chinese Medicine(ZYYCXTD-D-202204)
  • China Postdoctoral Science Foundation(2023M742627)
  • Postdoctoral Fellowship Program of CPSF(GZC20231928)
  • Foundation of State Key Laboratory of Component-based Chinese Medicine(CBCM2023201)
Year 2025 volume 5 Issue 1
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Article Info
doi: 10.1097/HM9.0000000000000143
  • Receive Date:2024-09-04
  • Online Date:2026-11-04
  • Published:2025-03-25
Article Data
Affiliations
History
  • Received:2024-09-04
  • Accepted:2025-01-21
Funding
National Multidisciplinary Innovation Team of Traditional Chinese Medicine(ZYYCXTD-D-202204)
China Postdoctoral Science Foundation(2023M742627)
Postdoctoral Fellowship Program of CPSF(GZC20231928)
Foundation of State Key Laboratory of Component-based Chinese Medicine(CBCM2023201)
Affiliations
    1Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
    2Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
    3National Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, China
    4Italian National Institute of Health, Rome, Italy
    5Istituto Superiore di Sanità, Centro Eccellenza Clinica, Qualità e Sicurezza delle Cure, Rome, Italy

Corresponding:

* Junhua Zhang, E-mail: ;
Alice Josephine Fauci, E-mail: .
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