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驾驭医疗保健人工智能治理:风险与公平的综合算法监督与管理框架

Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.

作者信息

Kumar Rahul, Sporn Kyle, Waisberg Ethan, Ong Joshua, Paladugu Phani, Vadhera Amar S, Amiri Dylan, Ngo Alex, Jagadeesan Ram, Tavakkoli Alireza, Loftus Timothy, Lee Andrew G

机构信息

Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, USA.

University of Massachusetts Chan School of Medicine, Worcester, USA.

出版信息

Health Care Anal. 2025 Aug 13. doi: 10.1007/s10728-025-00537-y.

Abstract

Integrating artificial intelligence (AI) in healthcare has sparked innovation but exposed vulnerabilities in regulatory oversight. Unregulated "shadow" AI systems, operating outside formal frameworks, pose risks such as algorithmic drift, bias, and disparities. The Comprehensive Algorithmic Oversight and Stewardship (CAOS) Framework addresses these challenges, combining risk assessments, data protection, and equity-focused methodologies to ensure responsible AI implementation. This framework offers a solution to bridge oversight gaps while supporting responsible healthcare innovation. CAOS functions as both a normative governance model and a practical system design, offering a scalable framework for ethical oversight, policy development, and operational implementation of AI systems in healthcare.

摘要

将人工智能(AI)整合到医疗保健领域引发了创新,但也暴露了监管监督方面的漏洞。不受监管的“影子”AI系统在正式框架之外运行,带来了诸如算法漂移、偏差和差异等风险。综合算法监督与管理(CAOS)框架应对这些挑战,结合风险评估、数据保护和以公平为重点的方法,以确保AI的负责任实施。该框架提供了一种解决方案,既能弥合监督差距,又能支持负责任的医疗保健创新。CAOS既作为一种规范性治理模式,又作为一种实用的系统设计,为医疗保健领域AI系统的道德监督、政策制定和运营实施提供了一个可扩展的框架。

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