期刊
EBIOMEDICINE
卷 67, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ebiom.2021.103358
关键词
Health disparities; Artificial intelligence; Machine learning; Health policy; Race/ethnicity; Genetic ancestry; Sex; Gender
资金
- NSF [1942926, 1763191]
- NIH [P30AG059307, U01MH098953]
AI has the potential to impact human health in various ways, but challenges such as non-representative samples and narrow evaluation metrics need to be addressed. Short-term solutions such as diverse data collection and ongoing monitoring, as well as long-term structural changes, may help mitigate these challenges.
Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges. (C) 2021 The Authors. Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据