Tina Hernandez-Boussard (
Shazia Mehmood Siddique, University of Pennsylvania, Philadelphia, Pennsylvania.
Health Aff (Millwood). 2023 Oct;42(10):1369-1373. doi: 10.1377/hlthaff.2023.00545.
As the use of artificial intelligence has spread rapidly throughout the US health care system, concerns have been raised about racial and ethnic biases built into the algorithms that often guide clinical decision making. Race-based medicine, which relies on algorithms that use race as a proxy for biological differences, has led to treatment patterns that are inappropriate, unjust, and harmful to minoritized racial and ethnic groups. These patterns have contributed to persistent disparities in health and health care. To reduce these disparities, we recommend a race-aware approach to clinical decision support that considers social and environmental factors such as structural racism and social determinants of health. Recent policy changes in medical specialty societies and innovations in algorithm development represent progress on the path to dismantling race-based medicine. Success will require continued commitment and sustained efforts among stakeholders in the health care, research, and technology sectors. Increasing the diversity of clinical trial populations, broadening the focus of precision medicine, improving education about the complex factors shaping health outcomes, and developing new guidelines and policies to enable culturally responsive care are important next steps.
随着人工智能在美国医疗保健系统中的广泛应用，人们对算法中存在的种族和民族偏见表示担忧，这些算法通常指导着临床决策。基于种族的医学依赖于使用种族作为生物差异的替代物的算法，导致了不适当、不公正和对少数族裔有害的治疗模式。这些模式导致了健康和医疗保健方面的持续差距。为了缩小这些差距，我们建议采用一种关注种族的临床决策支持方法，考虑到结构性种族主义和健康的社会决定因素等社会和环境因素。医学专业协会的近期政策变化和算法开发方面的创新代表了在消除基于种族的医学方面取得的进展。成功需要医疗保健、研究和技术部门的利益相关者持续承诺和持续努力。增加临床试验人群的多样性、扩大精准医学的关注范围、改善对塑造健康结果的复杂因素的教育、制定新的指南和政策以实现文化响应性护理，这些都是重要的下一步。
