The persistent underrepresentation of racial and ethnic minority groups in clinical trials remains a critical barrier to achieving global health equity. While biomedical innovation progresses at an exponential rate, the benefits of these advancements are frequently distributed unevenly due to systemic biases in patient recruitment, trial design, and geographical accessibility. This research explores the convergence of Artificial Intelligence (AI), Machine Learning (ML), and decentralized clinical trial (DCT) methodologies as transformative tools for enhancing equity, diversity, and inclusion (EDI). By synthesizing data from the Veterans Health Administration, cardiovascular outcome trials, and nephrology research, this study identifies specific demographic gaps, such as the disproportionate mortality rates and transplant waitlisting disparities affecting Black individuals. The paper evaluates how AI can optimize patient identification through Electronic Health Records (EHR) and how telemedicine can bridge the "place-based" disparities that hinder participation in low-resource settings. Furthermore, the research investigates the role of genetic ancestry, specifically West African lineage, in modulating therapeutic responses, arguing that diverse enrollment is a scientific necessity rather than a mere regulatory checkbox. The proposed framework advocates for a multi-layered approach-integrating AI-driven recruitment, decentralized infrastructure, and regulatory foresight-to ensure that the next decade of drug development is both technologically advanced and socially just.
Leveraging Artificial Intelligence and Decentralized Methodologies to Mitigate Racial and Ethnic Disparities in Clinical Research: A Comprehensive Framework for Health Equity
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References
Abbidi, S.R., Sinha, D. AI/ML-based strategies for enhancing equity, diversity, and inclusion in randomized clinical trials. Trials (2026). https://doi.org/10.1186/s13063-026-09537-2
Brennen, A., Tidor, B., Choudhury, A. Mapping the landscape of AI in clinical trials: challenges and future directions. J Clin Res Bioeth., 13 (3) (2022), Article 100030, 10.1016/j.jcrbe.2022.100030
Dorsey, E.R., Topol, E.J. Telemedicine 2020 and the next decade. Lancet, 395 (10227) (2020), p. 859, 10.1016/S0140-6736(20)30424-4
European Medicines Agency. Regulatory science to 2025: Strategic reflection. 2021. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/regulatory-science-2025-strategic-reflection_en.pdf.
Golestaneh, L. et al. The role of place in disparities affecting black men receiving hemodialysis. Kidney Int. Rep. 6, 357–365 (2021).
Makady, A., van Veelen, A., Jonker, C.M., Goettsch, W.G. Applications of AI across the drug development lifecycle: current trends and future directions. Front. Pharmacol., 13 (2022), Article 842791, 10.3389/fphar.2022.842791
Makanga, M., Kumar, V., Janssens, Y. Decentralized clinical trials in low-resource settings: Opportunities and challenges. Contemp. Clin. Trials Commun., 31 (2023), Article 101079, 10.1016/j.conctc.2023.101079
Peterson, K., Anderson, J., Boundy, E., Ferguson, L., McCleery, E. & Waldrip, K. Mortality disparities in racial/ethnic minority groups in the Veterans Health Administration: an evidence review and map. Am. J. Public Health 108, e1–e11 (2018).
Rao, S. et al. Association of genetic west African ancestry, blood pressure response to therapy, and cardiovascular risk among self-reported black individuals in the systolic blood pressure reduction intervention trial (SPRINT). JAMA Cardiol. 6, 388–398 (2021).
Reese, P.P. et al. Racial disparities in preemptive waitlisting and deceased donor kidney transplantation: ethics and solutions. Am. J. Transplant. 21, 958–967 (2021).
Topol, E. The convergence of AI and medicine: the future of clinical trials. Nat Digit Med., 6 (2023), p. 45, 10.1038/s41746-023-00800-w
United States Renal Data System (USRDS). 2021 USRDS Annual Data Report. 2021.
Umeukeje, E.M. & Young, B.A. Genetics and ESKD disparities in African Americans. Am. J. Kidney Dis. 74, 811–821 (2019).
Walraven, C., Demeulemeester, J. Leveraging AI to improve patient recruitment in clinical trials. Trials, 22 (2021), p. 672, 10.1186/s13063-021-05693-9
World Health Organization (WHO). Health Topics. Health Equity. 2022.
Zhu, J.W. et al. Global representation of heart failure clinical trial leaders, collaborators, and enrolled participants: a bibliometric review 2000-2020. Eur. Heart J. Qual. Care Clin. Outcomes. 8, 659–669 (2022).