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杜克大学 :2026医疗系统中的AI安全:基础设施建设与风险管理实践强化白皮书(中译版)(13页).pdf

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1、White PaperAI Safety in Health Systems:Building Infrastructure and Strengthening Risk Management PracticesMarch 26,2026healthpolicy.duke.eduAI Safety in Health Systems:Building Infrastructure and Strengthening Risk Management Practices2AuthorsCameron Joyce,MPA,Duke-Margolis Institute for Health Poli

2、cy Nicoleta J Economou,PhD,Duke Health AI Evaluation and Governance Christina Silcox,PhD,Duke-Margolis Institute for Health PolicyAcknowledgmentsThe authors would like to thank several individuals for their contributions to this white paper.First,we thank the participants of our expert workshop,who

3、are listed at the end of the paper,for sharing their expertise and experiences,as well as the multiple other health system representatives and policy influencers that held individual informational calls with us.We would also like to thank Hannah Vitello,Luke Durocher,and Michelle Langlois for their

4、help with this paper and the associated meetings,and Laura Hughes for design support.Any opinions expressed in this paper are solely those of the authors and do not necessarily represent the views or policies of any other person or organization external to Duke-Margolis.This work was funded by the D

5、uke Endowment.About the Duke-Margolis Institute for Health Policy The Robert J.Margolis,MD,Institute for Health Policy at Duke University is directed by Mark McClellan,and brings together expertise from the Washington,DC,policy community,Duke University,and Duke Health to address the most pressing i

6、ssues in health policy.The mission of Duke-Margolis is to improve health,health equity,and the value of health care through practical,innovative,and evidence-based policy solutions.Duke-Margolis catalyzes Duke Universitys leading capabilities,including interdisciplinary academic research and capacit

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1. **AI安全风险管理必要性**:临床AI工具存在性能漂移、偏见、幻觉等风险,传统患者安全监测系统难以检测,需建立全生命周期风险管理框架。 2. **治理与问责**:健康系统需明确AI治理结构,定义工具所有权与问责制,建立集中化AI工具清单,并制定安全事件上报流程(如Duke Health整合AI标志到安全报告系统)。 3. **部署与监控**:预部署需评估风险、制定缓解措施(如人工审核)及监控计划;后部署需跟踪性能漂移、安全事件,并评估缓解措施有效性。 4. **跨系统学习**:通过行业协作(如CHAI、ACR注册表)共享最佳实践,减少重复工作,提升整体安全性。 5. **政策支持**:需明确监管框架(如FDA与ONC职责),激励安全基础设施投资,避免“AI鸿沟”。
AI安全如何保障? 医疗AI风险何在? AI治理怎么做?
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