在学术医疗中心的公平性和AI治理
Paige Nong1, Reema Hamasha, Jodyn Platt
1Division of Health Policy and Management, University of Minnesota School of Public Health, 516 Delaware St SE, Minneapolis, MN 55455.
The American journal of managed care
|May 31, 2024
概括
学术医学中心很少考虑人工智能治理中的公平性. 大多数人认为偏见是一种工具特征,忽视了更广泛的患者影响和威胁健康公平的资源差异.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能治理管理 人工智能治理
- 健康 公平 研究 健康 公平 研究
背景情况:
- 学术医疗中心越来越多地采用人工智能和机器学习用于医疗保健应用.
- 人们越来越担心AI/ML有可能延续或加剧现有的健康不平等.
- 了解这些机构如何管理AI/ML,特别是关于公平性的理解,对于负责任的实施至关重要.
研究的目的:
- 调查在学术医疗中心的人工智能/机器学习治理流程中考虑公平性的程度.
- 探索学术医疗中心如何处理预测技术的治理和整合公平考虑.
主要方法:
- 在学术医疗中心与关键人员 (首席医疗信息官) 进行半结构面试的定性分析.
- 数据来自17个代表13个机构的个人,来自美国四个人口普查地区.
- 研究的重点是AI/ML治理以及这些过程中的公平考虑.
主要成果:
- 少数参与者积极考虑AI/ML治理中的不平等,种族主义或偏见.
- 大多数参与者将股权问题视为固有的工具特征 (例如,算法偏见,公平性).
- 较少的参与者考虑了AI/ML对患者的更广泛影响,健康IT资源差异被认为是对公平的重大威胁.
结论:
- 学术医疗中心缺乏对AI/ML治理中的公平的持续考虑,尽管他们意识到潜在的不平等.
- 优先考虑公平素养,设计强有力的监督政策,并促进对AI/ML工具的关键参与是必不可少的.
- 需要采取积极的措施,防止不平等在数字医疗保健系统中根深蒂固.
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