Artificial Intelligence-Enabled Strategies for Enhancing Equity, Diversity, and Inclusion in Randomized Clinical Trials: Integrating Digital Health Infrastructure, Epidemiological Disparities, and Regulatory Innovation

Abstract

Persistent inequities in randomized clinical trials (RCTs) compromise the generalizability, ethical integrity, and scientific validity of biomedical research. Disparities in enrollment by race, ethnicity, age, and socioeconomic status reflect broader structural inequities in healthcare, including those observed in mortality outcomes, chronic disease burden, oncology genomics, and infectious disease sequelae. The rapid digitization of healthcare and advances in artificial intelligence (AI) and machine learning (ML) offer unprecedented opportunities to redesign clinical trial ecosystems.
A structured, integrative research synthesis was conducted based on peer-reviewed studies, governmental guidance, and technological frameworks. The methodology synthesizes evidence on mortality disparities, disease epidemiology, genomic heterogeneity, electronic health record (EHR) adoption, cloud computing, federated learning, and regulatory digital health policies. These domains were analytically mapped onto stages of the clinical trial lifecycle.
The findings indicate that AI-driven eligibility optimization, predictive recruitment modeling, EHR-based phenotyping, federated multi-institutional collaboration, and cloud-enabled trial management can substantially mitigate structural barriers to participation. When aligned with regulatory digital innovation frameworks and ethical safeguards, AI systems can address underrepresentation in oncology, cardiometabolic disorders, infectious disease outcomes, and vaccine research. However, algorithmic bias, data fragmentation, and unequal digital access remain critical challenges.
AI/ML technologies, embedded within robust regulatory and ethical governance structures, can reconfigure RCTs toward structural inclusivity. Achieving equity requires not only technical innovation but also deliberate integration of epidemiological knowledge, cost-sensitive trial design, and digital infrastructure harmonization. Future research must empirically validate these frameworks in multi-national contexts.