公平的ICP:识别偏见和增加在后实施临床决策支持的护理点的透明度,使用诱导性合规预测
Xiaotan Sun1,2, Makiya Nakashima1,2, Christopher Nguyen1,2
1Cardiovascular Innovation Research Center, Cleveland Clinic, Cleveland, OH 44195, United States.
概括
公平ICP通过为各种患者群体提供可靠的不确定性量化来增强人工智能 (AI) 临床决策支持系统 (CDSS). 这种AI公平性框架提高了准确性,并减少了护理点的偏见.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 临床决策支持系统 临床决策支持系统
- 健康 公平 卫生 公平
背景情况:
- 由人工智能 (AI) 驱动的临床决策支持系统 (CDSS) 由于偏见而面临可信性问题,导致不同患者亚群的绩效差异.
- 现有的公平缓解策略缺乏可扩展性和可访问性,未能在护理点确保公平.
研究的目的:
- 介绍FairICP,一个后实施框架,使用诱导性符合预测 (ICP) 来解决CDSS中的AI公平性.
- 提供可操作的模型不确定性的知识,这种不确定性是由对公平医疗保健的亚种群偏见引起的.
主要方法:
- 公平ICP采用ICP进行特定集团校准,以定义模型能力界限.
- 在心脏MRI,胸部X射线和皮肤病成像数据集 (公共和私人) 上进行评估.
- 评估预测绩效提升和不公平减轻.
主要成果:
- 与基线相比,FairICP的预测准确度提高了7.2%.
- 在数据集中平均减少了2.2%的亚群之间的准确性差距.
- 在值得信赖的能力边界内,证明了预测可靠性的提高.
结论:
- 公平ICP提供了一个强大的解决方案,以促进对AI-CDSS的信任和透明度.
- 促进不同患者群体在医疗保健中的平等和公平.
- 强调后处理方法对于AI-CDSS实施和监控的重要性.
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