Matching clinicians with clinical trials using AI

Junyi Gao, Cao Xiao, Lucas M. Glass, Ewen M. Harrison, Jimeng Sun

Abstract: Clinical trial-site selection is often inefficient, leading to low enrolment, poor participant diversity and costly delays. We developed DocTr, a cross-modal deep learning framework to optimize this process. DocTr uniquely integrates patient encounter data from medical claims, unstructured trial documents and historical enrolment relationships from OpenPayments data to recommend clinician investigators, specifically optimizing for recommendation accuracy, demographic fairness and operational efficiency. Evaluated on 24,984 clinicians and 5,210 trials, DocTr achieved 58% higher match similarity than leading baselines. A genetic optimization algorithm further refines recommendations, improving fairness scores related to patient race and ethnicity by up to 25% compared with the ground-truth enrolment while minimizing competing trials to near zero. DocTr also provides accurate recruitment cost estimations. By making site selection substantially more efficient, accurate and fair, this model offers a powerful method to accelerate patient access to new therapies. Using data involving 24,984 clinicians and 5,210 trials, an AI model integrates information from medical claims, unstructured trial documents and historical enrolment relationships to suggest optimal trial-site configurations, to enhance cost-effectiveness and ethnicity representation.

External IDs:doi:10.1038/s44360-026-00073-6