Two early 2025 regulatory milestones mark a turning point for AI in clinical research. The FDA released its first draft guidance on AI in drug development after reviewing more than 500 submissions containing AI components since 2016. That same month, ICH E6(R3) reached Step 4 adoption, establishing a risk-based framework that explicitly accommodates electronic health records, wearables, and decentralized trials.
The operational pressure is clear. With over 530,000 studies registered on ClinicalTrials.gov and enrollment timelines historically running nearly double planned lengths, AI is no longer theoretical. Machine learning models now assist with protocol design by analyzing historical criteria patterns, rank candidate sites using performance data and disease prevalence, and screen EHR records via FHIR to surface eligible patients who might otherwise be missed.
The published evidence base has grown substantially. A 2025 narrative review examined AI applications across the trial lifecycle, while a separate scoping review analyzed 142 studies on AI in risk assessment. Most evidence remains retrospective or single-institution, and prospective validation across therapeutic areas is still limited. AI tools do not replace clinical judgment but reduce mechanical review time and apply consistent screening logic, though they can replicate biases present in underlying data.