公平医疗成像AI的局限性 在现实世界的概括化中
Yuzhe Yang1, Haoran Zhang2, Judy W Gichoya3
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. yuzhe@mit.edu.
Nature medicine
|June 28, 2024
概括
医疗成像中的人工智能 (AI) 使用人口快捷方式,可能导致不公平. 具有较少人口编码的模型在新环境中显示出更好的公平性,指导公平的AI部署.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 医疗保健的不平等 医疗保健的不平等
- 算法公平性 算法公平性
背景情况:
- 人工智能 (AI) 在医学成像中接近人类水平的性能.
- 人工智能可以从医疗图像中推断出人口统计数据,引发人们对医疗保健差异的担忧.
- 以前的研究强调了AI在预测中使用人口快捷方式的潜力.
研究的目的:
- 调查医学AI对人口编码的程度.
- 在不同亚群和数据集的AI模型中分析公平性差异.
- 确定在临床环境中公平部署人工智能的最佳实践.
主要方法:
- 在放射学,皮肤学和眼科领域对AI模型的分析.
- 包括来自六个全球胸部X射线数据集的数据.
- 公平性和绩效的评价在分配和外部测试集.
主要成果:
- 证实医学成像AI利用人口快捷方式进行疾病分类.
- 对快捷方式的算法校正在本地提高了公平性,但在新的测试设置中没有.
- 在外部评估中,人口属性编码较少的模型显示出更高的全球公平性.
结论:
- 医疗人工智能模型可以通过人口快捷方式无意中延续医疗保健差异.
- 改善本地公平性并不能保证全球最佳性或在新环境中的公平性.
- 优先考虑人口编码较少的模型对于在不同的人口和地点进行强大和公平的AI临床部署至关重要.
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