Integrating Artificial Intelligence, Regulatory Diversity Mandates, and Federated Learning Architectures to Advance Equity and Interpretability in Global Randomized Clinical Trials

Abstract

Background: Randomized clinical trials (RCTs) are the cornerstone of evidence-based medicine, yet persistent underrepresentation of racial and ethnic minorities, restrictive eligibility criteria, and geographic inequities challenge their external validity. Concurrently, artificial intelligence (AI), digital medicine platforms, and federated learning architectures are transforming clinical research infrastructures. Regulatory agencies, including the US Food and Drug Administration (FDA) and the Pharmaceutical and Medical Devices Agency (PMDA), have issued guidance emphasizing global trial harmonization and participant diversity. However, systematic integration of AI methodologies with diversity mandates remains conceptually and operationally underdeveloped.

Objective: This study develops a comprehensive theoretical and regulatory-informed framework for leveraging AI, deep learning, federated learning, and interpretable machine learning to enhance equity, diversity, and inclusion in global RCTs while aligning with regulatory guidance and ethical oversight mechanisms.

Methods: A qualitative integrative analysis synthesizing regulatory documents, empirical studies on racial participation in trials, machine learning methodologies, and interpretability science was conducted. Conceptual modeling mapped the interactions among diversity mandates, AI-driven recruitment and outcome prediction, digital medicine infrastructures, electronic informed consent, and federated data architectures.

Results: Findings indicate that AI can support equitable RCT design across five domains: predictive enrollment modeling, adaptive eligibility optimization, federated cross-border data harmonization, interpretable decision-support systems, and digital consent facilitation. Regulatory guidance from FDA and PMDA provides a structural backbone for operationalizing diversity plans, yet lacks detailed technical standards for AI deployment. Federated learning emerges as a promising mechanism to reconcile global data diversity with privacy constraints. Interpretability science provides essential safeguards for accountability in AI-assisted trial management.

Conclusion: The convergence of AI technologies and regulatory diversity mandates offers unprecedented opportunities to transform global RCT equity. However, achieving this transformation requires deliberate alignment among algorithm design, regulatory compliance, ethical oversight, and interpretability science. Without such integration, AI risks reinforcing structural disparities rather than remedying them.