Department of Biomedical Data Science, Stanford University, United States.
History of Science, Stanford University, United States.
EBioMedicine. 2021 May;67:103358. doi: 10.1016/j.ebiom.2021.103358. Epub 2021 May 4.
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.
人工智能（AI）有可能影响人类健康的许多方面，从基础研究发现到个人健康评估。至关重要的是，这些技术进步能让来自世界各地的不同人群广泛受益。但这具有挑战性，因为 AI 算法通常是在非代表性样本上开发的，并根据狭隘的指标进行评估。在这里，我们概述了生物医学 AI 在结果设计、数据收集和技术评估方面的主要挑战，并以精准健康为例说明了在每个阶段可能出现的偏差和健康差异。然后，我们提出了短期方法（更多样化的数据收集和 AI 监测）和长期结构变化（在资金、出版物和教育方面）来解决这些挑战。
