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

Hongyu Chen¹, Xiaohan Li¹, Xing He^2,3

¹Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL.

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|June 9, 2025

Summary

Large language models (LLMs) are revolutionizing clinical trial recruitment by improving patient-trial matching. These advanced AI systems enhance accuracy and scalability, addressing key bottlenecks in trial execution.

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Area of Science:

Artificial Intelligence in Clinical Research
Biomedical Informatics
Clinical Trial Operations

Background:

Patient recruitment is a significant challenge in clinical trials, often leading to delays and failures.
Inefficient patient-trial matching contributes to these recruitment difficulties.
Large language models (LLMs) offer potential solutions for automating and optimizing patient-trial matching.

Purpose of the Study:

To conduct a scoping review of emerging applications of LLMs in patient-trial matching.
To synthesize current research on LLM integration for improving clinical trial recruitment.

Main Methods:

A comprehensive literature search was performed in PubMed, Web of Science, and OpenAlex (December 2022 - December 2024).
Studies integrating LLMs into patient-trial matching systems were included.
Data extraction focused on system architecture, data processing, eligibility criteria, matching techniques, and performance evaluation.

Main Results:

24 studies met the inclusion criteria, with a surge in publications in 2024.
Most systems utilized patient-centric matching and generative pretrained transformer models.
LLM approaches showed improved accuracy and scalability but faced challenges in performance variability, interpretability, and data dependency.

Conclusions:

LLM-based systems offer a transformative approach to enhance clinical trial recruitment efficiency and accuracy.
Addressing limitations in generalizability, explainability, and data constraints is crucial for future optimization.
Advancements in hybrid modeling, domain-specific fine-tuning, and real-world data integration will further streamline trial matching and execution.