审计模型可以抑制糟糕的人工智能预测,可以改善人类-人工智能协作性能.
Katherine E Brown1, Jesse O Wrenn1,2, Nicholas J Jackson1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Journal of the American Medical Informatics Association : JAMIA
|January 13, 2026
概括
机器学习 (ML) 抑制提高了人类-人工智能合作的公平性和性能. 审计ML预测与不确定性量化提高了医疗保健中的协作决策.
科学领域:
- 医疗保健人工智能的人工智能
- 临床决策支持系统 临床决策支持系统
- 机器学习公平性 机器学习公平性
背景情况:
- 机器学习 (ML) 在医疗保健中越来越多地使用,但可以在各个子群体中表现出不公平.
- 临床医生过度依赖ML可以延续现有的偏见.
- 抑制ML,使不可靠的预测保持沉默,显示了减轻这些问题的潜力.
研究的目的:
- 评估ML抑制对临床医生和AI之间的合作公平性的影响.
- 评估ML不确定性作为审计ML性能的一种指标.
主要方法:
- 利用了来自范德比尔特大学医学中心和MIMIC-IV-ED的电子健康记录数据.
- 预测患者的结果 (死亡,ICU转移,30天再入院) 使用梯度增强树和一个预言模型.
- 模拟的临床医生接受ML预测和测量性能 (AUC) 和公平性 (平均几率差).
主要成果:
- 在ML表现优越时,ML抑制优于单独的人类临床医生的表现,而不会降低公平性.
- 当临床医生优于ML时,抑制并没有显著降低公平性.
- 整合不确定性量化改进了基于抑制的方法.
结论:
- 通过抑制来审计ML预测显示了增强人类-人工智能协作性能和公平性的承诺.
- 抑制有效地解决了由于过度依赖非最佳的ML模型而产生的问题.
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