审计模型可以抑制糟糕的人工智能预测,可以提高人类-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, Tennessee.
medRxiv : the preprint server for health sciences
|July 16, 2025
概括
机器学习 (ML) 抑制可以改善临床医生和人工智能之间的合作公平性和性能. 用不确定性量化审计ML预测可以增强这种效果,减轻医疗保健AI中的不公平.
科学领域:
- 医疗保健中的人工智能
- 机器学习公平性 机器学习公平性
- 人类-人工智能合作
背景情况:
- 机器学习 (ML) 模型越来越多地用于医疗保健决策.
- 机器学习模型可能表现不公平,导致患者亚群的结果不一致.
- 临床医生过度依赖ML可能会延续或加剧现有的不公平.
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不确定性量化在审计ML预测中的作用.
- 改善医疗保健中人类-人工智能合作的性能和公平性.
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.
- 基于经验数据的模拟临床医生决策和使用AUC测量绩效,使用平均几率差的公平性.
主要成果:
- 在ML超越人类时,ML抑制改善了协作性能 (p < 0.034),而不会降低公平性.
- 当人类表现优于ML时,抑制提高了表现 (p < 5.2 × 10^-5),但人类的决策更公平 (p < 0.0019).
- 将不确定性量化整合到抑制方法中,提高了整体性能.
结论:
- 通过审计师模型抑制低质量的ML预测显示了增强人类-AI合作的潜力.
- 这种方法可以提高AI系统在临床环境中的性能和公平性.
- 不确定性量化是审计ML和改进协作成果的宝贵工具.
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