Were Martin C, Li Ang, Malin Bradley A, Yin Zhijun, Coco Joseph R, Collins Benjamin X, Clayton Ellen Wright, Novak Laurie L, Hendricks-Sturrup Rachele, Oluyomi Abiodun O, Anders Shilo, Yan Chao
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
Department of Biomedical Informatics, Vanderbilt University Medical Center, Suite 750, 2525 West End Ave, Nashville, TN, United States, 1 6153229374.
J Med Internet Res. 2025 Jul 31;27:e73996. doi: 10.2196/73996.
The role and use of race within health-related artificial intelligence (AI) and machine learning (ML) models have sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking. This perspective provides a broad-based, systematic, and crosscutting landscape analysis of race-related challenges, structured around the AI and ML life cycle and framed through "points to consider" to support inquiry and decision-making.
种族在与健康相关的人工智能（AI）和机器学习（ML）模型中的作用及应用引发了越来越多的关注和争议。尽管相关问题复杂且广泛，但仍缺乏一个强大而全面的框架来指导利益相关者进行审视和解决。本观点提供了一个基于广泛、系统且贯穿各领域的种族相关挑战的全景分析，围绕人工智能和机器学习的生命周期构建，并通过“需考虑要点”进行阐述，以支持调查和决策。
