生成式人工智能辅助心血管疾病诊疗决策的信任构建与挑战OA

In cardiovascular diagnosis and treatment,generative artificial intelligence(Generative AI)demonstrates significant potential in efficiently generating medical records,providing personalized diagnostic and therapeutic recommendations,and optimizing physician-patient communication.However,its inherent"black-box"nature,unpredictable hallucination phenomena,and complex medical ethics have precipitated a crisis of trust among clinicians and healthcare administrators.This paper systematically explores the specific requirements for AI accuracy,interpretability,and robustness in the cardiovascular field from both technical and non-technical perspectives.On the technical front,the paper analyzes interpretability methods such as attention visualization,thought-chain prompts,and Retrieval-Augmented Generation(RAG),as well as the role of uncertainty quantification in risk warning.On the non-technical front,it emphasizes the critical importance of rigorous clinical validation,cross-jurisdictional regulatory frameworks,and the delineation of liability,as well as human-AI collaboration models and the cultivation of AI literacy among physicians for build-ing trust.The article further summarizes the current technical bottlenecks,data challenges,and barriers to acceptance,and proposes future directions involving technological convergence,collaborative decision-making,and dynamic trust frameworks.Trust is a multidi-mensional systems engineering endeavor that requires the coordinated advancement of technology,institutional frameworks,and humanistic considerations to achieve the reliable implementation of generative AI in cardiovascular clinical practice.