Abstract

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.

中文翻译：

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解决食品和药物管理局人工智能和机器学习监管框架中的健康差异。

摘要

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，再加上机器学习等数据分析工具的发展，为利用这些数据缩小健康差异提供了机会。然而，这些工具也被证明会加剧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对作为医疗设备的软件进行监管的一部分。使用食品和药物管理局 (FDA) 提出的用于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有助于预测和减轻群体伤害。