Artificial intelligence in traditional Chinese medicine: from systems biological mechanism discovery, real-world clinical evidence inference to personalized clinical decision support.
10.1016/S1875-5364(25)60983-6
- Author:
Dengying YAN
1
;
Qiguang ZHENG
1
;
Kai CHANG
1
;
Rui HUA
1
;
Yiming LIU
1
;
Jingyan XUE
1
;
Zixin SHU
2
;
Yunhui HU
3
;
Pengcheng YANG
3
;
Yu WEI
3
;
Jidong LANG
3
;
Haibin YU
4
;
Xiaodong LI
2
;
Runshun ZHANG
5
;
Wenjia WANG
6
;
Baoyan LIU
7
,
8
;
Xuezhong ZHOU
9
Author Information
1. Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
2. Institute of Liver Diseases, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, Hubei 430061, China.
3. Tianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin 300410, China.
4. Department of Respiratory Diseases, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450003, China.
5. Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
6. Tianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin 300410, China. Electronic address: tsl-wangwenjia@tasly.com.
7. China Academy of Chinese Medical Sciences, Beijing, 100700, China. Electronic address: liubaoyan@
8. com.
9. Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China. Electronic address: xzzhou@bjtu.edu.cn.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Clinical decision support;
Real-world clinical evidence;
Systems biological mechanism
- MeSH:
Medicine, Chinese Traditional/methods*;
Artificial Intelligence;
Humans;
Precision Medicine;
Decision Support Systems, Clinical
- From:
Chinese Journal of Natural Medicines (English Ed.)
2025;23(11):1310-1328
- CountryChina
- Language:English
-
Abstract:
Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
