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.

Artificial Intelligence, Clinical Trials, Health Equity, Racial Disparities,

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