Key points are not available for this paper at this time.
Generative artificial intelligence (AI), specifically large language models, such as DeepSeek, has accelerated the digital transformation of healthcare systems in both developing and developed countries. The use of AI in diagnostics, image processing and interpretation, treatment personalization, clinical documentation, and drug discovery is an example of the implementation of AI in Western medicine. The need for evidence-based studies and a standardized approach to scientific medicine aligns well with these applications. AI can leave a lasting impact on the Chinese medicine (CM) landscape by increasing expectations and presenting new challenges. The analogy between the CM-specific diagnostic methods and syndrome differentiation, which is holistic, pattern-oriented, patient-centered, and clinical data analysis, is significant at multiple levels. These qualities pose challenges for AI usage in CM, which heavily relies on structured data and pattern recognition. Despite these adversities, AI can still be used in CM through data standardization, prediction formulation, and treatment planning, provided that the integration of this tool considers the primary principles of CM and adheres to ethical and regulatory considerations. This review examines the dichotomous approach to health and medicine in the contexts of AI and CM, highlighting the evolving potential, inherent limitations, and ethical and regulatory issues associated with the application of AI to CM. It provides a foundation for developing technologically progressive yet culturally and philosophically sensitive strategies that are in harmony with traditional clinical values.
Lim et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: