Revolutionizing traditional Chinese medicine research: Advancements in traditional machine learning and deep learning for AI-driven applications

Jiahe Ren; Xinyu Shi; Ruiheng Liang; Yi Qin; Xun Gao; Qing Zhang; Longshan Zhao; Xuefeng Guan

doi:10.1016/j.jpha.2026.101572

Article Contents

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Jiahe Ren, Xinyu Shi, Ruiheng Liang, Yi Qin, Xun Gao, Qing Zhang, Longshan Zhao, Xuefeng Guan. Revolutionizing traditional Chinese medicine research: Advancements in traditional machine learning and deep learning for AI-driven applications[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2026.101572

Citation:

Jiahe Ren, Xinyu Shi, Ruiheng Liang, Yi Qin, Xun Gao, Qing Zhang, Longshan Zhao, Xuefeng Guan. Revolutionizing traditional Chinese medicine research: Advancements in traditional machine learning and deep learning for AI-driven applications[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2026.101572

Citation:

Jiahe Ren, Xinyu Shi, Ruiheng Liang, Yi Qin, Xun Gao, Qing Zhang, Longshan Zhao, Xuefeng Guan. Revolutionizing traditional Chinese medicine research: Advancements in traditional machine learning and deep learning for AI-driven applications[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2026.101572

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Revolutionizing traditional Chinese medicine research: Advancements in traditional machine learning and deep learning for AI-driven applications

doi: 10.1016/j.jpha.2026.101572

a. School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, 110016, China;
b. School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China;
c. Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China;
d. School of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China;
e. Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang, Jiangsu, 222001, China;
f. Department of Gastroenterology, Lianyungang Affiliated Hospital of Nanjing University of Chinese Medicine, Lianyungang, Jiangsu, 222004, China;
g. School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China

Funds:

This work was supported by the Liaoning Provincial Natural Science Foundation of China (Grant No.: 2024-MS-088), the Youqing Lift Program of Shenyang Pharmaceutical University (Grant No.: YQ202206), and the Middle-aged Backbone Personnel Training Program of Shenyang Pharmaceutical University (Grant No.: ZQN2016011).

Received Date: Jun. 19, 2025
Accepted Date: Jan. 30, 2026
Rev Recd Date: Jan. 26, 2026
Available Online: Feb. 03, 2026

Abstract

Abstract

Due to the numerous limitations of traditional methods in the research of traditional Chinese medicine (TCM), since entering the information age, artificial intelligence (AI) has not only played an increasingly important role in people's lives, but its components—traditional machine learning (ML) and deep learning (DL)—have also developed rapidly and demonstrated significant advantages and potential in TCM research. Therefore, they have been widely applied in areas such as TCM toxicity prediction, TCM efficacy assessment, targeted TCM development, identification and classification of TCM decoction pieces, authenticity and quality verification, quality control, and exploration of prescription composition rules. This article reviews and summarizes the specific applications of AI based on traditional ML and DL in the abovementioned research aspects in recent years, and identifies the challenges faced in the research. The aims are to summarize the current research status of AI in the field of TCM, clarify the technical methods employed, gain insights into the technical challenges encountered during the application process and ponder over them, and provide references and bases for future research in related aspects. At the same time, it explores the future applications trends of AI in TCM research, expounds on the importance of interdisciplinary cooperation in promoting the development of this field, looks ahead to its broad application prospects, and contributes to achieving more innovations and breakthroughs in TCM research.
- Artificial intelligence,
- Traditional machine learning,
- Deep learning,
- Traditional Chinese medicine

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References(150)

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