Matching Patients to Clinical Trials with Large Language Models

Understand

Patient recruitment is challenging for clinical trials.

• We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models.
• TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matching); and finally generates trial-level scores (TrialGPT-Ranking).
• We evaluate TrialGPT on three cohorts of 183 synthetic patients with over 75,000 trial annotations.