算法个人公平和医疗保健:一个范围审查
Joshua W Anderson1, Shyam Visweswaran1,2
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15213, United States.
JAMIA open
|December 31, 2024
概括
算法个人公平性 (IF) 研究正在出现,特别是在医疗保健应用中. 需要更多的研究来定义和评估IF方法,以确保公平的临床护理.
科学领域:
- 医疗保健人工智能的人工智能
- 算法公平性 算法公平性
- 减轻偏见的缓解偏见
背景情况:
- 医疗保健中的人工智能 (AI) 算法可能会延续和放大现有的差异.
- 对于公平的临床护理,有必要理解和减轻算法偏见.
- 算法个人公平 (IF) 为解决这些问题提供了一个有希望的方法.
研究的目的:
- 进行算法个人公平性 (IF) 的范围审查.
- 了解IF的当前指标,方法和医疗保健应用.
- 确定IF医疗保健的研究缺口和未来方向.
主要方法:
- 对四个IF研究数据库 (PubMed,ACM,IEEE,medRxiv) 的范围审查.
- 搜索仅限于2013年1月至2024年11月期间发表的文章.
- 综合了来自32篇文章的数据.
主要成果:
- 确定的主题:哲学基础,IF指标,缓解方法,群体与个人公平的含义,以及医疗保健应用.
- 关于FI的研究还处于起步阶段,特别是在医疗保健领域,出版物有限.
- 医疗保健应用IF很少,但显示稳定增长.
结论:
- 算法IF研究,定义和应用在医疗保健领域处于早期阶段.
- 挑战包括主观相似性定义和潜在的数据驱动偏见.
- 进一步的综合性研究对于在医疗保健环境中推进IF至关重要.
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