发表机构
UT Health McWilliams School of Biomedical Informatics; Informatics Review LLC; Luminant Consulting; University of California, San Francisco; Waymark（UT健康麦威廉姆斯生物医学信息学院; 信息学审查有限责任公司; 光辉咨询公司; 加州大学旧金山分校; Waymark公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对临床AI融入现实护理后现有安全机制的不足，提出AI M&M框架，通过四维度分类审查临床AI故障，以补充现有监测与报告机制，推动机构学习。
AI 中文摘要
临床人工智能正越来越多地融入现实医疗护理，但现有的安全机制难以从单个AI相关错误和未遂事故中进行重构与学习。聚合模型监测可识别性能变化，传统患者安全报告可记录不良事件，但两者均无法解释风险如何在AI系统、临床医生、工作流程及机构管控的交互中产生。我们提出AI发病率与死亡率（AI M&M），这是一种用于基于案例审查临床AI故障的结构化无责框架。该框架结合标准化案例录入、证据保存与调查人员层面的重构、工具参与式归因及纠正措施跟踪。每个事件按四个关联维度分类：触发因素-机制-临床路径-纠正措施，将暴露漏洞的条件与产生风险的过程、其对护理的后果及分配的补救措施区分开来。我们使用5个门诊用药及临床决策支持案例演示该框架；两名临床医生审查者独立应用全部四个分类轴，在所有20个轴级分类中达成一致。AI M&M旨在补充而非替代模型监测、患者安全报告及监管监督，通过将工作流中单个AI故障转化为可操作的机构学习，尚需在机构、AI系统及临床场景中进行前瞻性评估。
英文摘要
Clinical artificial intelligence is increasingly embedded in real-world care, yet existing safety mechanisms are poorly suited to reconstructing and learning from individual AI-related errors and near-misses. Aggregate model monitoring can identify performance changes, and traditional patient safety reporting can capture adverse events, but neither is designed to explain how risk emerges across the interaction among AI systems, clinicians, workflows, and institutional controls. We propose AI Morbidity and Mortality (AI M&M), a structured, blameless framework for case-based review of clinical AI failures. The framework combines standardized case intake, evidence preservation and investigator-level reconstruction, tool-in-loop attribution, and corrective-action tracking. Each event is classified across four linked dimensions: Trigger - Mechanism - Clinical Pathway - Corrective Action, separating the condition that exposed a vulnerability from the process that produced risk, its consequence for care, and the remediation assigned. We demonstrate the framework using five illustrative outpatient medication and clinical decision-support cases; two clinician reviewers independently applied all four classification axes and reached agreement across all 20 axis-level classifications. AI M&M is intended to complement, rather than replace, model monitoring, patient safety reporting, and regulatory oversight by converting individual AI-in-workflow failures into actionable institutional learning. Prospective evaluation across institutions, AI systems, and clinical settings is needed.
