Abstract 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 Outdoor Coffee Table (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.TrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of the initial collection.
Manual evaluations on 1015 patient-criterion pairs show that TrialGPT-Matching achieves an accuracy of 87.3% with faithful explanations, close to the expert performance.The TrialGPT-Ranking scores are highly correlated with Spices human judgments and outperform the best-competing models by 43.8% in ranking and excluding trials.Furthermore, our user study reveals that TrialGPT can reduce the screening time by 42.
6% in patient recruitment.Overall, these results have demonstrated promising opportunities for patient-to-trial matching with TrialGPT.