AI医疗工具应支持医生真实诊疗思维，而非追求虚高指标。
Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
- 以医生认知与决策为核心设计AI辅助系统
- 强调临床实际影响，而非实验室超人表现
- 适合关注AI落地应用的临床研究者
人工智能有望革新医学，但其实际影响受限于普遍存在的转化鸿沟。我们指出，当前以技术为中心的方法论是这一挑战的根本原因，使这些系统与临床实践（尤其是诊断推理和决策）本质上不兼容。为此，我们提出一种新型的社会技术概念框架，旨在构建数据驱动的支持工具，以补充医生的认知与知识活动。关键在于，该框架优先考虑真实世界的应用效果，而非在无关紧要的基准上达到超人性能。
原文摘要 · Abstract (English)
Artificial intelligence promises to revolutionise medicine, yet its impact remains limited because of the pervasive translational gap. We posit that the prevailing technology-centric approaches underpin this challenge, rendering such systems fundamentally incompatible with clinical practice, specifically diagnostic reasoning and decision making. Instead, we propose a novel sociotechnical conceptualisation of data-driven support tools designed to complement doctors' cognitive and epistemic activities. Crucially, it prioritises real-world impact over superhuman performance on inconsequential benchmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。