  Matching cancer patients to clinical trials is essential for advancing
treatment and patient care. However, the inconsistent format of medical free
text documents and complex trial eligibility criteria make this process
extremely challenging and time-consuming for physicians. We investigated
whether the entire trial matching process - from identifying relevant trials
among 105,600 oncology-related clinical trials on clinicaltrials.gov to
generating criterion-level eligibility matches - could be automated using Large
Language Models (LLMs). Using GPT-4o and a set of 51 synthetic Electronic
Health Records (EHRs), we demonstrate that our approach identifies relevant
candidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0%
when matching patient-level information at the criterion level against a
baseline defined by human experts. Utilizing LLM feedback reveals that 39.3%
criteria that were initially considered incorrect are either ambiguous or
inaccurately annotated, leading to a total model accuracy of 92.7% after
refining our human baseline. In summary, we present an end-to-end pipeline for
clinical trial matching using LLMs, demonstrating high precision in screening
and matching trials to individual patients, even outperforming the performance
of qualified medical doctors. Our fully end-to-end pipeline can operate
autonomously or with human supervision and is not restricted to oncology,
offering a scalable solution for enhancing patient-trial matching in real-world
settings.
