Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matching

Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research using text-only models has struggled to address this problem in a reliable and scalable way due to (1) limited reasoning capabilities, (2) information loss from converting visual records to text, and (3) lack of a generic EHR integration to extract patient data.

We introduce a broadly applicable, integration-free, LLM-powered pipeline that automates patient-trial matching using unprocessed documents extracted from EHRs. Our approach leverages (1) the new reasoning-LLM paradigm, enabling the assessment of even the most complex criteria, (2) the visual capabilities of the latest LLMs to interpret medical records without lossy image-to-text conversions, and (3) multimodal embeddings for efficient medical record search. The pipeline was validated on the n2c2 2018 cohort selection dataset (288 diabetic patients) and a real-world dataset composed of 485 patients from 30 different sites matched against 36 diverse trials.

On the n2c2 dataset, our method introduces a new state-of-the-art criterion-level accuracy of 93%. In real-world trials, the pipeline yielded an accuracy of 87%, undermined by the difficulty of replicating human decision-making when medical records lack sufficient information. Nevertheless, users were able to review overall eligibility in under 9 minutes per patient on average, representing an 80% improvement over traditional manual chart reviews.

This pipeline demonstrates robust performance in clinical trial patient matching without requiring custom integration with site systems or trial-specific tailoring, thereby enabling scalable deployment across sites seeking to leverage AI for patient matching.