DREAM:一个框架,用于发现人工智能对受保护属性的预测背后的机制
Soham U Gadgil1, Alex J DeGrave1,2, Joseph D Janizek3
1Paul G. Allen School of Computer Science and Engineering, University of Washington.
medRxiv : the preprint server for health sciences
|August 8, 2025
概括
人工智能 (AI) 模型可以从医学图像中预测种族和性别等受保护的属性. 一个新的框架,DREAM,有助于解释这些AI机制,揭示临床应用中的潜在风险,并提高AI透明度.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 可解释的人工智能 (XAI)
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 人工智能 (AI) 越来越多地用于医学成像,其模型能够识别敏感的人口特征.
- 解释AI模型如何预测这些属性对于安全可靠的临床部署至关重要.
- 现有的方法缺乏全面的框架来理解AI在医疗环境中的决策.
研究的目的:
- 介绍DREAM (发现和解释AI机制),一个一般可解释的AI (XAI) 框架.
- 解释AI模型如何从医疗图像中预测受保护的人口特征.
- 量化识别信号对AI分类表现的贡献.
主要方法:
- 训练有素的高性能AI分类器用于从胸部X射线预测种族和从皮肤镜损伤预测性别.
- 采用各种XAI技术来识别人口特征的预测信号.
- 开发并应用"通过平衡去除"技术来量化信号贡献.
主要成果:
- 在预测种族 (~0.96 ROC-AUC) 和性别 (~0.78 ROC-AUC) 中取得了高性能.
- 证明了人口快捷方式如何对下游诊断任务产生负面影响.
- 量化信号贡献,解释了~15%的放射学和~42%的皮肤病学性能.
结论:
- 梦想提供了一个框架,用于解释AI对医疗成像中受保护属性的预测.
- 强调了人口缩短路径的风险和需要谨慎部署人工智能的需要.
- 提高了医疗保健的AI驱动诊断工具的透明度和可靠性.
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