Rahul Kumar, Kyle Sporn, Ethan Waisberg, Joshua Ong, Phani Paladugu, Amar S Vadhera, Dylan Amiri, Alex Ngo, Ram Jagadeesan, Alireza Tavakkoli, Timothy Loftus, Andrew G Lee
{"title":"导航医疗保健人工智能治理：风险和公平的综合算法监督和管理框架。","authors":"Rahul Kumar, Kyle Sporn, Ethan Waisberg, Joshua Ong, Phani Paladugu, Amar S Vadhera, Dylan Amiri, Alex Ngo, Ram Jagadeesan, Alireza Tavakkoli, Timothy Loftus, Andrew G Lee","doi":"10.1007/s10728-025-00537-y","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":46740,"journal":{"name":"Health Care Analysis","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.\",\"authors\":\"Rahul Kumar, Kyle Sporn, Ethan Waisberg, Joshua Ong, Phani Paladugu, Amar S Vadhera, Dylan Amiri, Alex Ngo, Ram Jagadeesan, Alireza Tavakkoli, Timothy Loftus, Andrew G Lee\",\"doi\":\"10.1007/s10728-025-00537-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":46740,\"journal\":{\"name\":\"Health Care Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Care Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10728-025-00537-y\",\"RegionNum\":3,\"RegionCategory\":\"哲学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ETHICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Care Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10728-025-00537-y","RegionNum":3,"RegionCategory":"哲学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ETHICS","Score":null,"Total":0}
Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.
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.
期刊介绍：
Health Care Analysis is a journal that promotes dialogue and debate about conceptual and normative issues related to health and health care, including health systems, healthcare provision, health law, public policy and health, professional health practice, health services organization and decision-making, and health-related education at all levels of clinical medicine, public health and global health. Health Care Analysis seeks to support the conversation between philosophy and policy, in particular illustrating the importance of conceptual and normative analysis to health policy, practice and research. As such, papers accepted for publication are likely to analyse philosophical questions related to health, health care or health policy that focus on one or more of the following: aims or ends, theories, frameworks, concepts, principles, values or ideology. All styles of theoretical analysis are welcome providing that they illuminate conceptual or normative issues and encourage debate between those interested in health, philosophy and policy. Papers must be rigorous, but should strive for accessibility – with care being taken to ensure that their arguments and implications are plain to a broad academic and international audience. In addition to purely theoretical papers, papers grounded in empirical research or case-studies are very welcome so long as they explore the conceptual or normative implications of such work. Authors are encouraged, where possible, to have regard to the social contexts of the issues they are discussing, and all authors should ensure that they indicate the ‘real world’ implications of their work. Health Care Analysis publishes contributions from philosophers, lawyers, social scientists, healthcare educators, healthcare professionals and administrators, and other health-related academics and policy analysts.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
