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Artif Intell Cancer. Sep 8, 2026; 7(1): 121344
Published online Sep 8, 2026. doi: 10.35713/aic.121344
Artificial intelligence empowering precision oncology: The convergence and innovation of traditional Chinese medicine and Western medicine wisdom
Chang Qiao, Yu-Tong Han, Jiang-Ping Zhan, Jia Yuan, Xiao-Tong Tian, Yue-Chuan Jiao, Hao-Wei Li, Ling-Yong Wu, Chu Li, Yu-Xuan He, De-Hui Li
Chang Qiao, Jia Yuan, Xiao-Tong Tian, Yue-Chuan Jiao, Hao-Wei Li, Ling-Yong Wu, Chu Li, Yu-Xuan He, Graduate School, Hebei University of Chinese Medicine, Shijiazhuang 050091, Hebei Province, China
Yu-Tong Han, Department of Neurology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
Jiang-Ping Zhan, Department of Gastroenterology, Chengde Third Hospital, Chengde 067000, Hebei Province, China
De-Hui Li, Department of Oncology II, The First Affiliated Hospital of Hebei University of Chinese Medicine, Hebei Province Hospital of Chinese Medicine, Key Laboratory of Integrated Chinese and Western Medicine for Gastroenterology Research, Hebei Industrial Technology Institute for Traditional Chinese Medicine Preparation, Shijiazhuang 050000, Hebei Province, China
Co-first authors: Chang Qiao and Yu-Tong Han.
Co-corresponding authors: Jiang-Ping Zhan and De-Hui Li.
Author contributions: Qiao C and Han YT contributed equally to this manuscript and are co-first authors. Li DH designed the overall concept and outline of the manuscript; Qiao C, Han YT, Zhan JP, Yuan J, and Tian XT contributed to the writing and editing of the manuscript, as well as drawing the figure; Jiao YC, Li HW, Wu LY, Li C and He YX reviewed the literature; Li DH directed and reviewed the paper; Zhan JP and Li DH played important and indispensable roles in the manuscript preparation as the co-corresponding authors; and all authors have read and approved the final manuscript.
AI contribution statement: The AI tools are only used for language optimization and formatting assistance. There is no involvement of AI tools in the generation of research data, the interpretation of results, or the formulation of conclusions. All AI-generated outputs are critically reviewed and revised by the authors.
Supported by the Natural Science Foundation of Hebei Province, No. H2024423105; 2023 Government Funded Project of the Outstanding Talents Training Program in Clinical Medicine, No. ZF2023165; Key Research and Development Projects of Hebei Province, No. 18277731D; Hebei Provincial Administration of Traditional Chinese Medicine, Scientific Research Project, No. 2023045 and No. 2024023; Hebei Institute of Traditional Chinese Medicine Pharmaceutical Preparation Industry Technology Special Project, No. YJY2024006; and Scientific Research Project of Health Commission of Hebei Province, No. 20220962 and No. 20240282.
Conflict-of-interest statement: All the authors have declared that there are no relevant conflicts of interest.
Corresponding author: De-Hui Li, MD, Department of Oncology II, The First Affiliated Hospi
tal of Hebei University of Chinese Medicine, Hebei Province Hospital of Chinese Medicine, Key Laboratory of Integrated Chinese and Western Medicine for Gastroenterology Research, Hebei Industrial Technology Institute for Traditional Chinese Medicine Preparation, No. 389 Zhongshan East Road, Chang’an District, Shijiazhuang 050000, Hebei Province, China.
258289951@qq.com
Received: March 23, 2026
Revised: May 6, 2026
Accepted: June 8, 2026
Published online: September 8, 2026
Processing time: 164 Days and 3.7 Hours
The prevention and treatment of malignant tumors remain a major global health challenge. Owing to the marked heterogeneity and dynamic evolution of cancer, conventional diagnostic and therapeutic paradigms are increasingly inadequate for the demands of precision medicine. Artificial intelligence (AI) is accelerating the transition of comprehensive cancer care from experience-driven practice toward data-driven, dynamically supported decision-making. By leveraging machine learning, deep learning, natural language processing, knowledge graphs, graph neural networks, and multimodal data fusion, AI has been widely applied across key domains of oncology, including cancer screening, medical image interpretation, digital pathology analysis, molecular subtyping, treatment response assessment, and prognostic prediction. These advances provide new technical pathways for individualized diagnosis and treatment as well as whole-course disease management. At the same time, traditional Chinese medicine (TCM), with its holistic view and treatment based on syndrome differentiation, offers unique theoretical and practical advantages in comprehensive cancer prevention and treatment. However, modern TCM research has long been constrained by the limited objectification of syndromes, substantial heterogeneity in clinical data, complex mechanisms underlying the actions of Chinese herbs, and the lack of standardized evaluation systems. In recent years, AI has been increasingly applied in TCM research, including knowledge mining from classical medical texts and clinical records, syndrome identification, analysis of herbal compatibility rules, screening of active constituents, prediction of therapeutic targets, and construction of efficacy evaluation models. These developments provide a novel methodological foundation for integrating TCM with modern precision oncology. Against this background, this review systematically summarizes the integration pathways, application value, and practical challenges of AI in integrated TCM and Western medicine for cancer treatment, with the aim of providing a theoretical basis and practical reference for the development of intelligent integrated precision oncology.
Core Tip: Advances in artificial intelligence (AI) offer novel research strategies and promising applications for the precision diagnosis and treatment of cancer within the framework of integrated traditional Chinese medicine (TCM) and Western medicine. Through the systematic integration and deep mining of multimodal data spanning molecular biology, medical imaging, clinical pathology, and TCM syndromes, AI may help reveal the complex relationships between tumor heterogeneity and the host’s overall physiological state. This, in turn, may facilitate the development of a new integrative oncology paradigm that combines disease diagnosis with syndrome differentiation and unifies holistic concepts with individualized interventions, thereby supporting improved clinical efficacy, optimized therapeutic decision-making, and the realization of precision medicine.
