Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework
Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework
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DOI：
10.1093/jamia/ocaa133
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发表时间：
2020-12-01
影响因子：
6.4
通讯作者：
Ferryman, Kadija
中科院分区：
文献类型：
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作者：
Ferryman, Kadija
来自设备、健康应用程序和电子健康记录的健康数据呈指数级增长，加上机器学习等数据分析工具的发展，为利用这些数据来缓解健康差距提供了机会。然而，这些工具也被证明加剧了边缘化群体面临的不平等。关注健康差异应该是良好的机器学习实践和医疗器械软件监管的一部分。使用美国食品和药物管理局（FDA）提出的规范医学机器学习工具的框架，我表明，在上市前和上市后审查阶段解决健康差异可以帮助预测和减轻群体伤害。
The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.