Survey Examines Potential of Large Language Models in Clinical Trial Recruitment

Global: Survey Examines Potential of Large Language Models in Clinical Trial Recruitment

Researchers Shrestha Ghosh, Moritz Schneider, Carina Reinicke, and Carsten Eickhoff posted a comprehensive survey on arXiv on June 18, 2025, later revised on December 30, 2025, that evaluates how large language models (LLMs) could be leveraged to improve clinical trial recruitment. The paper, titled “A Survey on LLM‑Assisted Clinical Trial Recruitment,” aims to map existing methods, benchmark resources, and implementation challenges for matching patients with trial protocols.

Motivation and Context

Recent breakthroughs in LLM capabilities have markedly advanced general‑domain natural language processing, yet their integration into high‑stakes domains such as clinical research remains modest. The authors note that trial eligibility criteria are typically expressed in natural language, while patient records combine structured fields with unstructured clinical notes, creating a fertile use case for sophisticated language understanding.

Trial‑Patient Matching Challenges

The core task examined is the alignment of trial descriptions with individual patient data—a process traditionally handled by rule‑based or narrowly trained machine‑learning systems. Because both inputs involve nuanced linguistic constructs, the survey highlights the need for models that can aggregate dispersed medical knowledge and perform reasoning across heterogeneous data sources.

LLM‑Based Approaches

Compared with trial‑specific pipelines, LLMs offer a more generalizable framework capable of consolidating distributed biomedical knowledge. The authors discuss emerging methods that prompt or fine‑tune LLMs to extract eligibility criteria, generate patient eligibility scores, and even suggest trial modifications. However, many of these prototypes rely on proprietary models, limiting reproducibility.

Evaluation Gaps

The survey critically assesses current benchmarking practices, observing that existing datasets often lack realistic diversity and that evaluation metrics are inconsistently applied. Consequently, the authors argue that the field lacks a robust, open‑source benchmark suite that can reliably compare LLM‑driven solutions against traditional baselines.

Barriers to Adoption

Key obstacles identified include data privacy constraints, the need for regulatory compliance, and the scarcity of high‑quality annotated corpora. Additionally, the authors point out that integrating LLMs into clinical workflows demands explainability and validation procedures that are not yet standardized.

Outlook and Recommendations

Looking forward, the paper proposes several research directions: developing open, domain‑specific LLMs; establishing transparent evaluation frameworks; and fostering collaborations between AI developers, clinicians, and regulatory bodies. By addressing these gaps, the authors suggest that LLMs could eventually streamline recruitment, reduce trial delays, and broaden patient access to experimental therapies.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.