在AI中提高公平性,用于胸部X射线分类
Nicholas J Jackson1, Chao Yan2, Bradley A Malin1,2
1Vanderbilt University, Nashville, TN.
概括
医学中的人工智能 (AI) 可以有偏见. 过量采样和合成数据增强在AI诊断预测中减少了多达74.7%的种族差异,而不影响性能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 健康 公平 卫生 公平
背景情况:
- 人工智能 (AI) 在医疗决策方面提供了潜在的改进.
- 人工智能算法可能会表现出偏见,导致对特定人口群体的不公平预测.
- 在MIMIC-CXR数据集中,人工智能诊断工具显示种族少数群体的虚假阴性率更高.
研究的目的:
- 评估提高AI诊断预测公平性的方法.
- 评估合成数据增强,过量抽样和基于人口统计的纠正的有效性.
- 为了确定公平性改进是否影响AI预测性能.
主要方法:
- 这项研究使用了MIMIC-CXR数据集,包括超过30万张胸部X射线图像.
- 评估的技术包括合成数据增强,过量采样和基于人口的纠正.
- 在应用这些方法之前和之后,测量了AI公平性和预测性表现 (AUC).
主要成果:
- 根据种族等人口统计特征调整预测并没有提高公平性或绩效.
- 过量采样使人工智能预测中的人口差异减少了74.7%.
- 合成数据增强使差异减少了10.6%,而整体性能没有显著下降.
结论:
- 过量采样和合成数据增强是缓解医学AI偏差的有效策略.
- 这些以数据为中心的方法可以提高公平性,而不会影响诊断准确性.
- 基于人口统计的纠正对于提高AI医疗保健应用中的公平性是无效的.
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