ArticleProceedings of the National Academy of Sciences of the United States of America2024
Race adjustments in clinical algorithms can help correct for racial disparities in data quality.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Bilateral Adrenal Primary Aldosteronism Is Uniformly Present in Hypertensive Afro-Caribbean First-Generation Immigrants to Western Canada.Journal of the American Heart Association · 2026Article
- Reevaluating the Use of Race/Ethnicity in the MESA Risk Score.Journal of the American Heart Association · 2026Article
- Article
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Effect of race and ethnicity on advanced breast cancer risk prediction model performance.NPJ digital medicine · 2025Article
- Potential for Algorithmic Bias in Clinical Decision Instrument Development.NPJ digital medicine · 2025Article
- Towards responsible artificial intelligence in healthcare-getting real about real-world data and evidence.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Evaluating the generalisability of formulas used to set tidal volumes in mechanically ventilated patients: an observational, multicohort, retrospective study.The Lancet. Respiratory medicine · 2025Observational
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Article
- Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates.Annual review of public health · 2025Review
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
- Article
- Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.NPJ digital medicine · 2024Article
- Clinical decisions, patient race, and flawed data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Enhancement of Fairness in AI for Chest X-ray Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Abstract
Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (
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