摘要Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empiric-al data over more than 2000 years,and now encompasses large-scale electronic medical re-cords(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI's potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direc-tion.This survey provides an insightful overview of TCM AI research,summarizing related re-search tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review high-lights representative AI methodologies alongside their applications in both TCM scientific in-quiry and clinical practice.To critically assess the current state of the field,this work identi-fies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of compre-hensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelli-gent,personalized decision support systems.