Horsfall Laura J, Bondaronek Paulina, Ive Julia, Poduval Shoba
Institute of Health Informatics, University College London, London, UK.
Clin Epidemiol. 2025 Jul 12;17:647-662. doi: 10.2147/CLEP.S527000. eCollection 2025.
Clinical algorithms are widely used tools for predicting, diagnosing, and managing diseases. However, race correction in these algorithms has faced increasing scrutiny for potentially perpetuating health disparities and reinforcing harmful stereotypes. This narrative review synthesizes historical, clinical, and methodological literature to examine the origins and consequences of race correction in clinical algorithms. We focus primarily on developments in the United States and the United Kingdom, where many race-based algorithms originated. Drawing on interdisciplinary sources, we discuss the persistence of race-based adjustments, the implications of their removal, and emerging strategies for bias mitigation and fairness in algorithm development. The practice began in the mid-19th century with the spirometer, which measured lung capacity and was used to reinforce racial hierarchies by characterizing lower lung capacity for Black people. Despite critiques that these differences reflect environmental exposure rather than inherited traits, the belief in race-based biological differences in lung capacity and other physiological functions, including cardiac, renal, and obstetric processes, persists in contemporary clinical algorithms. Concerns about race correction compounding health inequities have led many medical organizations to re-evaluate their algorithms, with some removing race entirely. Transitioning to race-neutral equations in areas like pulmonary function testing and obstetrics has shown promise in enhancing fairness without compromising accuracy. However, the impact of these changes varies across clinical contexts, highlighting the need for careful bias identification and mitigation. Future efforts should focus on incorporating diverse data sources, capturing true social and biological health determinants, implementing bias detection and fairness strategies, ensuring transparent reporting, and engaging with diverse communities. Educating students and trainees on race as a sociopolitical construct is also important for raising awareness and achieving health equity. Moving forward, regular monitoring, evaluation, and refinement of approaches in real-world settings are needed for clinical algorithms serve all patients equitably and effectively.
临床算法是用于预测、诊断和管理疾病的广泛使用的工具。然而，这些算法中的种族校正因可能使健康差距长期存在并强化有害的刻板印象而受到越来越多的审视。这篇叙述性综述综合了历史、临床和方法学文献，以研究临床算法中种族校正的起源和后果。我们主要关注美国和英国的发展情况，许多基于种族的算法都起源于那里。借鉴跨学科资料，我们讨论了基于种族的调整的持续存在、去除这些调整的影响，以及算法开发中减轻偏差和实现公平的新策略。这种做法始于19世纪中叶的肺活量计，它用于测量肺容量，并通过将黑人较低的肺容量特征化来强化种族等级制度。尽管有人批评这些差异反映的是环境暴露而非遗传特征，但对肺容量和其他生理功能（包括心脏、肾脏和产科过程）中基于种族的生物学差异的信念在当代临床算法中仍然存在。对种族校正加剧健康不平等的担忧导致许多医学组织重新评估其算法，一些组织完全去除了种族因素。在肺功能测试和产科等领域转向不考虑种族的方程在提高公平性而不影响准确性方面已显示出前景。然而，这些变化在不同临床背景下的影响各不相同，这凸显了仔细识别和减轻偏差的必要性。未来的努力应集中在纳入多样化的数据源、捕捉真正的社会和生物健康决定因素、实施偏差检测和公平策略、确保透明报告以及与不同社区互动。对学生和受训人员进行关于种族是一种社会政治建构的教育，对于提高认识和实现健康公平也很重要。展望未来，需要在现实环境中对方法进行定期监测、评估和改进，以使临床算法能够公平有效地为所有患者服务。
