Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. NEJM 385, 1737–1749 (2021).
Google Scholar
Eneanya, N. D., Yang, W. & Reese, P. P. Reconsidering the consequences of using race to estimate kidney function. JAMA 322, 113–114 (2019).
Google Scholar
Lu, J. H. et al. Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: a systematic review. JAMA Netw. Open 5, e2227779 (2022).
Google Scholar
Wright, J. L. et al. Eliminating race-based medicine. Pediatrics 150, e2022057998 (2022).
Google Scholar
Borrell, L. N. et al. Race and genetic ancestry in medicine—a time for reckoning with racism. NEJM 384, 474–480 (2021).
Google Scholar
Bonham, V. L., Callier, S. L. & Royal, C. D. Will precision medicine move us beyond race? NEJM 374, 2003–2005 (2016).
Google Scholar
Chokshi, D. A., Foote, M. M. K. & Morse, M. E. How to act upon racism-not race-as a risk factor. JAMA Health Forum 3, e220548 (2022).
Google Scholar
Ehdaie, B., Carlsson, S. & Vickers, A. Racial disparities in low-risk prostate cancer. JAMA 321, 1726–1727 (2019).
Google Scholar
Fletcher, S. A. et al. Geographic distribution of racial differences in prostate cancer mortality. JAMA Netw. Open 3, e201839 (2020).
Google Scholar
Aggarwal, R. et al. Diabetes screening by race and ethnicity in the United States: equivalent body mass index and age thresholds. Ann. Intern Med. 175, 765–773 (2022).
Google Scholar
Khor, S. et al. Racial and ethnic bias in risk prediction models for colorectal cancer recurrence when race and ethnicity are omitted as predictors. JAMA Netw. Open 6, e2318495 (2023).
Google Scholar
Gail, M. H. et al. Projecting individualized absolute invasive breast cancer risk in African American women. J. Natl Cancer Inst. 99, 1782–1792 (2007).
Google Scholar
Zink, A., Obermeyer, Z. & Pierson, E. Race adjustments in clinical algorithms can help correct forracial disparities in data quality. Proc. Natl Acad. Sci. USA 121, e2402267121 (2024).
Google Scholar
Bonner, S. N. et al. Clinical implications of removing race-corrected pulmonary function tests for African American patients requiring surgery for lung cancer. JAMA Surg. 158, 1061–1068 (2023).
Google Scholar
Landy, R. et al. Methods for using race and ethnicity in prediction models for lung cancer screening eligibility. JAMA Netw. Open 6, e2331155 (2023).
Google Scholar
Landy, R. et al. Using prediction-models to reduce persistent racial/ethnic disparities in draft 2020 USPSTF lung-cancer screening guidelines. J. Natl Cancer Inst. 113, 1590–1594 (2021).
Google Scholar
Manski, C. F. Patient-centered appraisal of race-free clinical risk assessment. Health Econ. 10, 2109–2114 (2022).
Google Scholar
Hougen, H. Y. et al. Adding a coefficient for race to the 4K score improves calibration for black men. J. Urol. 211, 392–399 (2024).
Google Scholar
Manski, C. F., Mullahy, J. & Venkataramani, A. S. Using measures of race to make clinical predictions: decision making, patient health, and fairness. Proc. Natl Acad. Sci. USA 120, e2303370120 (2023).
Google Scholar
Chohlas-Wood, A., Coots, M., Goel, S. & Nyarko, J. Designing equitable algorithms. Nat. Comput Sci. 3, 601–610 (2023).
Google Scholar
Wawira Gichoya, J., McCoy, L. G., Celi, L. A. & Ghassemi, M. Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inf. 28, e100289 (2021).
Google Scholar
Lin, J. S. et al. Addressing racism in preventive services: a methods project for the U.S. preventive services task force. (Agency for Healthcare Research and Quality, Rockville, MD, 2021).
Research Protocol: impact of healthcare algorithms on racial and ethnic disparities in health and healthcare. (Effective Health Care Program, Agency for Healthcare Research and Quality, Rockville, MD, Rockville, MD, 2022).
Evans, C. V., Johnson, E. S. & Lin, J. S. Assessing Algorithmic Bias and Fairness in Clinical Prediction Models for Preventive Services: A Health Equity Methods Project for the U.S. Preventive Services Task Force. (Agency for Healthcare Research and Quality, Portland, OR, 2023).
Section 1557 of the Affordable Care Act. in 42 (ed. Office for Civil Rights) (Office for Civil Rights, Washington, DC, 2022).
Paulus, J. K. & Kent, D. M. Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. NPJ Digit. Med. 3, 99 (2020).
Google Scholar
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).
Google Scholar
Obermeyer, Z. et al. Algorithmic Bias Playbook. (Center for Applied Artificial Intelligence, Chicago, IL, 2021).
Baeza-Yates, R. & Matthews, J. Statement on principles for responsible algorithmic systems. (ACM Technology Policy Office, Washington, DC, 2022).
Bates, D. W. How to regulate evolving AI health algorithms. Nat. Med. 29, 26 (2023).
Google Scholar
Daniels, N. Accountability for reasonableness. BMJ 321, 1300–1301 (2000).
Google Scholar
Majerol, M. & Hughes, D. L. CMS Innovation Center Tackles Implicit Bias. In Health Affairs Forefront (Health Affairs, 2022).
Khazanchi, R., Tsai, J., Eneanya, N. D., Han, J. & Maybank, A. Leveraging Affordable Care Act Section 1557 to address racism in clinical algorithms. in Health Affairs Forefront (Health Affairs, 2022).
Keith, K. HHS proposes revised ACA anti-discrimination rule. In Health Affairs Forefront (Health Affairs, 2022).
Shachar, C. & Gerke, S. Prevention of bias and discrimination in clinical practice algorithms. JAMA 329, 283–284 (2023).
Google Scholar
Ross, C. Amid the AI gold rush, a new company forms to vet models and root out weaknesses. In STAT (STAT News, 2023).
Schmidt, H., Gostin, L. O. & Williams, M. A. The Supreme Court’s rulings on race neutrality threaten progress in medicine and health. JAMA 330, 1033–1034 (2023).
Google Scholar
Harris, E. National health care leaders will develop AI code of conduct. JAMA 330, 401 (2023).
Google Scholar
Goodman, K. E., Morgan, D. J. & Hoffmann, D. E. Clinical algorithms, antidiscrimination laws, and medical device regulation. JAMA 329, 285–286 (2023).
Google Scholar
Centers for Medicare & Medicaid Services, Office for Civil Rights, Office of the Secretary & Department of Health and Human Services. Nondiscrimination in health programs and activities. Fed. Register 87, 47824–47920 (2022).
Blueprint for an AI bill of rights: making automated systems work for the American people. (ed. White House Office of Science and Technology Policy) (United States Government, Washington, DC, 2022).
de Hond, A. A. H. et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 5, 2 (2022).
Google Scholar
Sikstrom, L. et al. Conceptualising fairness: three pillars for medical algorithms and health equity. BMJ Health Care Inf. 29, e100459 (2022).
Google Scholar
Vasey, B. et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 377, e070904 (2022).
Google Scholar
Huang, J., Galal, G., Etemadi, M. & Vaidyanathan, M. Evaluation and mitigation of racial bias in clinical machine learning models: scoping review. JMIR Med. Inf. 10, e36388 (2022).
Google Scholar
Feng, J. et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 5, 66 (2022).
Google Scholar
Zou, J., Gichoya, J. W., Ho, D. E. & Obermeyer, Z. Implications of predicting race variables from medical images. Science 381, 149–150 (2023).
Google Scholar
Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-1) Proposed Rule. Vol. HTI-1 (ed. Office of the National Coordinator for Health Information Technology) (Office of the National Coordinator for Health Information Technology, Washington, DC, 2023).
Medlock, A. & Cooke, D. T. Removing structural racism in pulmonary function testing-why nothing is ever easy. JAMA Surg. 158, 1069 (2023).
Google Scholar
Suriyakumar, V. M., Ghassemi, M. & Ustun, B. When personalization harms performance: reconsidering the use of group attributes in prediction. PMLR 202, 33209–33228 (2023).
Coots, M., Saghafian, S., Kent, D. & Goel, S. Reevaluating the role of race and ethnicity in diabetes screening. In arXiv 2306.10220 (stat.AP) (arXiv, 2023).
Khazanchi, R., Marcelin, J., Abdul-Mutakabbir, J. & Essien, U. Race, racism, civil rights law, and the equitable allocation of scarce COVID-19 treatments. In Health Affairs Forefront (Health Affairs, 2022).
Kent, D. M., Ladin, K. & Duru, O. K. Equal treatment for equal risk: should race be included in allocation algorithms for Covid-19 therapies? In STAT (STAT News, 2022).
Shaikh, N. et al. Reassessment of the role of race in calculating the risk for urinary tract infection: a systematic review and meta-analysis. JAMA Pediatr. 176, 569–575 (2022).
Google Scholar
Grobman, W. A. et al. Prediction of vaginal birth after cesarean delivery in term gestations: a calculator without race and ethnicity. Am. J. Obstet. Gynecol. 225, 664.e661–664.e667 (2021).
Google Scholar
Inserro, A. Flawed racial assumptions in eGFR have care implications in CKD. In Am J Manag Care (The American Journal of Managed Care, 2020).
Steyerberg, E. W. et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med. 10, e1001381 (2013).
Google Scholar
Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350, g7594 (2015).
Google Scholar
Diao, J. A. et al. Implications of race adjustment in lung-function equations. NEJM 390, 2083–2097 (2024).
Google Scholar
Zhu, J., Brenna, C. T. A., McCoy, L. G., Atkins, C. G. K. & Das, S. An ethical analysis of clinical triage protocols and decision-making frameworks: what do the principles of justice, freedom, and a disability rights approach demand of us? BMC Med. Ethics 23, 11 (2022).
Google Scholar
Basu, A. Use of race in clinical algorithms. Sci. Adv. 9, eadd2704 (2023).
Google Scholar
Loftus, J. R., Russell, C., Kusner, M. J. & Silva, R. Causal reasoning for algorithmic fairness. In arXiv 1805.05859v05851 (cs.AI) (arXiv, 2018).
Wolff, R. F. et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann. Intern Med. 170, 51–58 (2019).
Google Scholar
Moher, D., Schulz, K. F., Simera, I. & Altman, D. G. Guidance for developers of health research reporting guidelines. PLoS Med. 7, e1000217 (2010).
Google Scholar
Dalkey, N. & Helmer, O. An experimental application of the delphi method to the use of experts. Manag. Sci. 9, 458–467 (1963).
Google Scholar
Gianfrancesco, M. A., Tamang, S., Yazdany, J. & Schmajuk, G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 178, 1544–1547 (2018).
Google Scholar
Park, Y. et al. Comparison of methods to reduce bias from clinical prediction models of postpartum depression. JAMA Netw. Open 4, e213909 (2021).
Google Scholar
Rajkomar, A., Hardt, M., Howell, M., Corrado, G. & Chin, M. Ensuring fairness in machine learning to advance health equity. Ann. Intern Med. 169, 866–872 (2018).
Google Scholar
Char, D. S., Shah, N. H. & Magnus, D. Implementing machine learning in health care – addressing ethical challenges. NEJM 378, 981–983 (2018).
Google Scholar
Parikh, R. B., Teeple, S. & Navathe, A. S. Addressing bias in artificial intelligence in health care. JAMA 322, 2377–2378 (2019).
Google Scholar
Flanagin, A., Frey, T. & Christiansen, S. L. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326, 621–627 (2021).
Google Scholar
Patton, M. Q. Qualitative Research & Evaluation Methods (SAGE Publications, Thousand Oaks, CA, 2002).
Saldana, J. The Coding Manual for Qualitative Researchers (SAGE Publications, London, 2013).
Office of Management and Budget, Office of Information and Regulatory Affairs & Executive Office of the President. Revisions to OMB’s statistical policy directive no. 15: standards for maintaining, collecting, and presenting federal data on race and ethnicity. Fed. Register 89, 22182–22196 (2024).
Chouldechova, A. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5, 153–163 (2017).
Google Scholar
Kleinberg, J., Mullainathan, S. & Raghavan, M. Inherent trade-offs in the fair determination of risk scores. in arXiv 1609.05807v05802 (cs. LG) (arXiv, 2016).
Balkin, J. M. & Siegel, R. B. The American civil rights tradition: anticlassification or antisubordination? Univ. Miami Law Rev. 58, 9–34 (2003).
Arnold, K. F. et al. Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning. Int. J. Epidemiol. 49, 2074–2082 (2021).
Google Scholar
Ramspek, C. L. et al. Prediction or causality? A scoping review of their conflation within current observational research. Eur. J. Epidemiol. 36, 889–898 (2021).
Google Scholar
Westreich, D. & Greenland, S. The Table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am. J. Epidemiol. 177, 292–298 (2013).
Google Scholar
King, M. L., Jr. ‘I Have A Dream’ Speech, In Its Entirety. In Talk of the Nation (NPR.org, 1963).
Corbie-Smith, G., Thomas, S. B. & St George, D. M. Distrust, race, and research. Arch. Intern Med. 162, 2458–2463 (2002).
Google Scholar
Armstrong, K. et al. Differences in the patterns of health care system distrust between blacks and whites. J. Gen. Intern Med. 23, 827–833 (2008).
Google Scholar
Jenkins, K. A., Keddem, S., Bekele, S. B., Augustine, K. E. & Long, J. A. Perspectives on racism in health care among black veterans with chronic kidney disease. JAMA Netw. Open 5, e2211900 (2022).
Google Scholar
Armstrong, K. et al. Prior experiences of racial discrimination and racial differences in health care system distrust. Med. Care 51, 144–150 (2013).
Google Scholar
Kent, D. M. et al. An electronic health record-compatible model to predict personalized treatment effects from the diabetes prevention program: a cross-evidence synthesis approach using clinical trial and real-world data. Mayo Clin. Proc. 97, 703–715 (2022).
Google Scholar
Corbett-Davies, S., Gaebler, J. D., Nilforoshan, H., Shroff, R. & Goel, S. The measure and mismeasure of fairness. JMLR 24, 1–117 (2023).
Van Calster, B. et al. A calibration hierarchy for risk models was defined: from utopia to empirical data. J. Clin. Epidemiol. 74, 167–176 (2016).
Google Scholar
Zanger-Tishler, M., Nyarko, J. & Goel, S. Risk scores, label bias, and everything but the kitchen sink. Sci. Adv. 10, eadi8411 (2024).
Google Scholar
Geiger, H. J. Racial and ethnic disparities in diagnosis and treatment: a review of the evidence and a consideration of causes. in Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (eds. Smedley, B. D., Stith, A. Y. & Nelson, A. R.) 417–454 (National Academies Press (US), Washington (DC), 2003).
Welch, H. G. & Fisher, E. S. Income and cancer overdiagnosis—when too much care is harmful. NEJM 376, 2208–2209 (2017).
Google Scholar
