Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches

作者：Hongyu Chen, Xiaohan Li, Xing He, Aokun Chen, James M. McGill, Emily C. Webber, Hua Xu, Mei Liu, Jiang Bian · 发表于：JCO Clinical Cancer Informatics · 年份：2025 · DOI：10.1200/cci-25-00071 · 被引用次数：7 · 研究领域：Machine Learning in Healthcare、Topic Modeling、Artificial Intelligence in Healthcare and Education

PURPOSE: Patient recruitment remains a major bottleneck in clinical trial execution, with inefficient patient-trial matching often causing delays and failures. Recent advancements in large language models (LLMs) offer a promising avenue for automating and improving this process. This scoping review aims to provide a comprehensive synthesis of the emerging applications of LLMs in patient-trial matching. METHODS: A comprehensive search was conducted in PubMed, Web of Science, and OpenAlex for literature published between December 1, 2022, and December 31, 2024. Studies were included if they explicitly integrated LLMs into patient-trial matching systems. Data extraction focused on system architectures, patient data processing, eligibility criteria processing, matching techniques, evaluation metrics, and performance. RESULTS: Of the 2,357 studies initially identified, 24 met the inclusion criteria. The majority (21/24) were published in 2024, highlighting the rapid adoption of LLMs in this domain. Most systems used patient-centric matching (17/24), with OpenAI's generative pretrained transformer models being the most commonly used LLM. Core components of these systems included eligibility criteria processing, patient data processing, and matching, with some incorporating retrieval algorithms to enhance computational efficiency. LLM-integrated approaches demonstrated improved accuracy and scalability in patient-trial matching, although challenges such as performance variability, i...