Advances in computational approaches to herbal prescription recommendation in traditional Chinese medicine: A review
10.1097/st9.0000000000000110
- Author:
Xin DONG
1
;
Geyan PAN
2
;
Juxian TANG
2
;
Xuchen ZHANG
3
;
Yutong HOU
2
;
Peng ZHANG
4
;
Xiaohan MAO
4
;
Zhipeng KE
4
;
Zongyao ZHAO
5
;
Xuezhong ZHOU
1
Author Information
1. Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing, China; State Key Laboratory of Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (Jiangsu Kanion Pharmaceutical Co.,Ltd. & Nanjing University of Chinese Medicine), Nanjing, China
2. Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing, China
3. Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing, China; Cangzhou People’s Hospital, Cangzhou, China
4. State Key Laboratory of Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (Jiangsu Kanion Pharmaceutical Co.,Ltd. & Nanjing University of Chinese Medicine), Nanjing, China
5. Traditional Chinese Medicine (TCM) Intelligent Diagnostics Research Center, Institute of Intelligent Traditional Chinese Medicine, Chongqing University of Chinese Medicine, Chongqing, China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Clinical decision support;
Deep learning;
Herbal prescription recommendation;
Large language models;
Traditional Chinese medicine
- From:
Science of Traditional Chinese Medicine
2026;4(2):119-131
- CountryChina
- Language:English
-
Abstract:
Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.
