AI-enabled clinical trials

Abstract

Randomized controlled trials remain the gold standard for evaluating the benefits and risks of new interventions, yet they face persistent challenges including high costs constraining sample sizes, complex eligibility criteria, recruitment difficulties, prolonged follow-up periods to capture hard end points, and the growing prevalence of poorly designed studies that create noise in literature. This Review proposes an artificial intelligence (AI)-enabled clinical trial engineering framework, applicable across medical specialties. This framework builds on four stages: assembling and harmonizing multimodal data to develop and validate tools for enabling trials; matching fit-for-purpose tools to the research question and target outcome; AI-supported trial conduct, including patient-to-trial matching, surrogate end points, externally matched comparator arms, digital twins, and automated data collection and curation; and faster, evidence-based go/no-go decisions that flag non-promising drugs early. Three overarching principles operate throughout: fit-for-purpose validation, continuous regulatory engagement and human oversight. This Review illustrates the framework through representative case studies, with the iBox surrogate in transplantation, the annualized relapse rate in multiple sclerosis, heart digital twins for ventricular tachycardia, AI-assisted histology (AIM-MASH) in hepatology and a large language model for patient-to-trial matching. It further maps the evolving regulatory landscape for AI-enabled clinical trials (EMA, FDA and the EU AI Act). Applied judiciously, AI-enabled clinical trials could shorten timelines, reduce costs and accelerate both the identification of effective therapies and the earlier elimination of futile ones. Artificial intelligence is poised to reshape clinical trials, addressing challenges in design, recruitment, monitoring and conduct. This Review outlines a framework built on deeply phenotyped cohorts, validated surrogate end points, digital twins and agentic AI systems that could shorten timelines and support the trial life cycle, highlighting opportunities to accelerate drug development while maintaining rigorous regulatory and benefit–risk assessment.