实验室测试中的种族差异作为人工智能偏见的潜在机制:在急诊室访问中进行匹配的队列分析
Trenton Chang1, Mark Nuppnau2, Ying He2
1Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
PLOS global public health
|October 30, 2024
概括
在急诊室的实验室检测中存在种族差异,白人患者更有可能接受完整的血液计数,代谢面板和血液培养. 这些差异可能会对人工智能 (AI) 模型产生偏见,可能会加剧健康不平等.
科学领域:
- 健康 公平 卫生 公平
- 临床决策支持系统 临床决策支持系统
- 人工智能在医学中的应用
背景情况:
- 医疗保健中的人工智能 (AI) 模型经常使用现有的实验室测试数据进行训练.
- 实验室测试中的种族差异可以在AI临床决策支持工具中引入偏见.
- 这种偏见可能会加剧现有的健康不平等现象,特别是对于代表性不足的种族群体.
研究的目的:
- 在急诊室 (ED) 的成年患者中量化实验室测试模式的种族差异.
- 评估这些差异对用于临床决策支持的AI模型的潜在影响.
- 调查种族在多发性障碍环境中影响使用常见实验室测试的程度.
主要方法:
- 采用了一种追溯的1:1准确匹配队列研究设计.
- 黑人和白人成年患者根据年龄,生物学性别,首席投诉和ED分拣得分进行匹配.
- 分析了来自美国两家主要教学医院紧急诊所访问的数据:密歇根医学和伯特以色列执事医疗中心.
主要成果:
- 与两个机构的黑人患者相比,白人患者对全血细胞计,代谢面板和血液培养的检测率显著更高.
- 相反,黑人患者的托罗邦尼检测率明显高于白人患者.
- 观察到的种族测试差异在统计学上是显著的,不同实验室测试和机构的百分比不同.
结论:
- 在急诊室实验室测试的使用中发现了显著的种族差异.
- 这些测试差异可能会在训练这些数据的AI模型中引入偏见的风险.
- 需要进一步的研究来了解这些差异的起源,并制定策略来减轻它们对人工智能驱动的医疗保健的影响.
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