TrialGPT: Matching Patients to Clinical Trials with Large Language Models

Presentation Time: 05:30 PM - 07:00 PM

Abstract Keywords: Large Language Models (LLMs), Artificial Intelligence, Natural Language Processing
Primary Track: Foundations

Clinical trial recruitment remains challenging due to complex eligibility criteria and fragmented patient data. We introduce TrialGPT, an AI-driven framework that leverages large language models to filter trials, interpret patient eligibility, and rank suitable studies. Evaluated on three available cohorts of synthetic patients, TrialGPT achieved 87.3% accuracy with criterion-level explanations, outperforming baseline methods by up to 51.6%. Our pilot user study demonstrated a 42.6% reduction in screening time, highlighting TrialGPT’s potential for streamlined patient recruitment.

Speaker(s):
Qiao Jin, M.D.
National Institutes of Health

Author(s):
Qiao Jin, M.D. - National Institutes of Health; Zifeng Wang, M.S. - University of Illinois Urbana-Champaign; Charalampos Floudas, MD, DMSc, MS - NIH/NCI; Nicholas Wan, Bachelor of Engineering - National Institutes of Health; Joey Chan, M.S. - National Library of Medicine; Fangyuan Chen, MD - University of Pittsburgh; Changlin Gong, MD - Albert Einstein College of Medicine; Dara Bracken-Clarke, MD - National Cancer Institute; Elisabetta Xue, MD - National Cancer Institute; Yin Fang, Ph.D. - National Institutes of Health; Shubo Tian, PhD - National Institutes of Health; Yifan Yang, B.S. - NCBI, NLM/NIH; Jimeng Sun - University of Illinois at Urbana Champaign; Zhiyong Lu, PhD - National Library of Medicine, NIH;