A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff - Yinger Gu; Xinyin Hu; Hye Won Lee; Zheng Yao; Tianyi Zhou; Nv Xia; Pingchun Yang; Jinglu Guo; Haifeng Huang; Lisi Wang; Wei Wang; Cheng Wang; et al. (Integrative Medicine Research)
Background: With the significant development of artificial intelligence (AI) in recent years, the inheritance and innovation of traditional Chinese medicine (TCM) urgently require the help of AI technology. The present study was to evaluate the attitudes and perceptions of medical staff towards the integration of TCM and AI development. Methods: A cross-sectional national survey was conducted at 13 medical institutions across China. A structured and self-reported questionnaire, consisting of six sections with a total of 14 items, was administered to 1100 medical staff between June 27th and July 11th, 2025. Results: In the process of clinical practice, 62.1 % of medical staff were willing to try TCM diagnosis and treatment services combined with AI. The top three important processes of integration of TCM and AI were medical research, personalized generation of regimen, and intelligent inquiry. The top three concerns about the potential risks associated with the integration of TCM and AI were the misinterpretation of cultural contexts, flexibility in dialectical treatment, and simplification of traditional TCM experience by algorithms. The top three most promising applications were the intelligent syndrome differentiation system (54.6 %), the TCM four diagnostic instruments (49.1 %), and the acupuncture and Tui Na robot (47.8 %). The top three most important factors in the application of AI in TCM were accuracy (78.0 %), convenience of operation (67.5 %), and participation of medical staff (60.9 %). Conclusion: The integration of TCM and AI has a brilliant and promising future, prioritizing diagnostic accuracy while addressing cultural/clinical adaptation challenges in key applications, such as syndrome differentiation systems.