Clinical trials are vital in advancing drug development and evidence-based
medicine, but their success is often hindered by challenges in patient
recruitment. In this work, we investigate the potential of large language
models (LLMs) to assist individual patients and referral physicians in
identifying suitable clinical trials from an extensive selection. Specifically,
we introduce TrialGPT, a novel architecture employing LLMs to predict
criterion-level eligibility with detailed explanations, which are then
aggregated for ranking and excluding candidate clinical trials based on
free-text patient notes. We evaluate TrialGPT on three publicly available
cohorts of 184 patients and 18,238 annotated clinical trials. The experimental
results demonstrate several key findings: First, TrialGPT achieves high
criterion-level prediction accuracy with faithful explanations. Second, the
aggregated trial-level TrialGPT scores are highly correlated with expert
eligibility annotations. Third, these scores prove effective in ranking
clinical trials and exclude ineligible candidates. Our error analysis suggests
that current LLMs still make some mistakes due to limited medical knowledge and
domain-specific context understanding. Nonetheless, we believe the explanatory
capabilities of LLMs are highly valuable. Future research is warranted on how
such AI assistants can be integrated into the routine trial matching workflow
in real-world settings to improve its efficiency.

研究使用大型语言模型（LLMs）协助患者和转诊医生从广泛的选择中筛选适合的临床试验，并评估了 TrialGPT 在 184 名患者和 18,238 个注释临床试验的实验结果，表明其高准确性和排名排除不合格候选试验的有效性。