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the claim
Healthcare algorithms can contain biases that negatively affect racial and ethnic populations.
the verdict
SUPPORTED
the evidence backs this
Recorded sources
10 sources for · 0 against
Is it true that healthcare algorithms can contain biases that negatively affect racial and ethnic populations?
Yes. Extensive research and systematic reviews demonstrate that healthcare algorithms frequently contain and propagate biases that negatively affect racial and ethnic populations.
Verdict: SUPPORTED · 10 sources for, 0 against · Last checked
The analysis
The retrieved papers overwhelmingly support the claim that healthcare algorithms contain biases impacting racial and ethnic populations. Multiple reviews, frameworks, and empirical studies document how AI and predictive models inherit systemic inequities, leading to disparities in clinical performance and patient outcomes. There are no refuting papers.
Evidence for · 10
Reviews framework principles to mitigate algorithm bias that leads to worse outcomes for racial and ethnic minoritized groups.
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Chin, M. H., Afsar-Manesh, N., Bierman, A. S., Chang, C. S., Colón-Rodríguez, C. J., Dullabh, P. M., Duran, D., Fair, M., Hernandez-Boussard, T., Hightower, M., Jain, A., Jordan, W. B., Konya, S., Moore, R. H., Moore, T. T., Rodriguez, R., Shaheen, G., Snyder, L., Srinivasan, M., Umscheid, C., & Ohno-Machado, L. (2023). Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. doi.org. https://doi.org/10.1001/jamanetworkopen.2023.45050
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More for · 9
Shows through scoping review how health AI algorithms can perpetuate health inequities for ethnic and racial groups.
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Hussain, S., Bresnahan, M., & Zhuang, J. (2024). The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review. doi.org. https://doi.org/10.1080/13557858.2024.2422848
Finds that racial disparities and biases across medical fields create a self-reinforcing cycle affecting patients.
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Sorice, V., Mortimore, G., Faghy, M., Sorice, R., & Tegally, D. (2025). The Perpetual Cycle of Racial Bias in Healthcare and Healthcare Education: A Systematic Review. doi.org. https://doi.org/10.1007/s40615-025-02417-6
Demonstrates baseline prediction disparities across demographic variables such as ethnicity in healthcare machine learning.
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A, O. & K, U. (2025). A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment.. doi.org. https://doi.org/10.1186/s13040-025-00505-1
Examines demographic biases regarding race in machine learning algorithms and strategies for mitigation.
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R, S., V, G., & JS, S. (2026). Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques.. doi.org. https://doi.org/10.1038/s41598-026-40555-x
Discusses how algorithmic bias in predictive tools can influence model performance and worsen maternal health disparities.
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SG, B., HA, S., YB, A., YH, A., MS, A., NI, A., S, S. M., NMK, E., MK, M., O, B., & O, D. (2026). Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.. doi.org. https://doi.org/10.3389/fdgth.2026.1841706
Proposes fairness frameworks for healthcare AI to navigate disparate impacts and biases.
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Jagtiani, P., Karabacak, M., & Margetis, K. (2024). A Concise Framework for Fairness: Navigating Disparate Impact in Healthcare AI (Preprint). doi.org. https://doi.org/10.2196/preprints.63465
Notes that algorithms frequently misclassify outcomes for racial and ethnic minorities across multiple medical domains.
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J, K., AB, A., J, M., M, K., J, A., D, F., & J, M. (2026). Artificial Intelligence and Algorithmic Bias in Burn Care: A Literature Review of Ethical Challenges.. doi.org. https://doi.org/10.1093/jbcr/irag105
Examines how healthcare AI systems inherit and amplify longstanding biases and health disparities for marginalized populations.
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J, D., I, F., A, O., & SA, F. (2026). Decoding Algorithmic Inequity: Reversing the Generative AI Racial Divide in Healthcare.. doi.org. https://doi.org/10.1016/j.clinthera.2026.06.015
Examines algorithmic fairness and performance gaps across racialized populations in machine learning models.
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I, I., B, G., R, O., & M, R. (2026). Algorithmic fairness in machine-learning models for determining the progression or recurrence of cardiovascular disease in racialized populations: a scoping review.. doi.org. https://doi.org/10.1093/jamiaopen/ooag138
What does the evidence say?
The retrieved papers overwhelmingly support the claim that healthcare algorithms contain biases impacting racial and ethnic populations. Multiple reviews, frameworks, and empirical studies document how AI and predictive models inherit systemic inequities, leading to disparities in clinical performance and patient outcomes. There are no refuting papers.
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