排除周期:加强医学上的差异
Ana Bracic1, Shawneequa L Callier2,3, W Nicholson Price4,5
1Department of Political Science, Michigan State University, East Lansing, MI, USA.
概括
医疗人工智能 (AI) 的发展受到排斥周期的阻碍. 这些循环涉及临床实践和数据收集,
科学领域:
- 医疗人工智能
- 医疗公平
- 临床信息学
背景情况:
- 临床实践和数据收集方法可能包含系统偏见.
- 现有的医疗人工智能 (AI) 模型可能会反映和放大这些偏见.
研究的目的:
- 分析临床实践,数据收集和医疗人工智能的相互联系.
- 找出这些因素是如何形成自我强化的排斥循环的.
主要方法:
- 对医疗保健中人工智能的现有文献进行概念分析.
- 检查临床工作流程,数据来源和算法开发之间的反循环.
主要成果:
- 临床实践,数据收集和医疗人工智能形成了相互影响的排除循环.
- 这些循环通过限制用于人工智能培训和验证的数据的多样性和代表性来延续不平等.
结论:
- 解决医疗人工智能中的排斥需要一个系统的方法.
- 干预措施必须针对临床实践和数据收集,以确保公平的AI开发和部署.
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