Cohort Discovery: A Survey on LLM-Assisted Clinical Trial Recruitment
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet,
their adoption in critical domains, such as clinical trial recruitment, remains
limited. As trials are designed in natural language and patient data is
represented as both structured and unstructured text, the task of matching
trials and patients benefits from knowledge aggregation and reasoning abilities
of LLMs. Classical approaches are trial-specific and LLMs with their ability to
consolidate distributed knowledge hold the potential to build a more general
solution. Yet recent applications of LLM-assisted methods rely on proprietary
models and weak evaluation benchmarks. In this survey, we are the first to
analyze the task of trial-patient matching and contextualize emerging LLM-based
approaches in clinical trial recruitment. We critically examine existing
benchmarks, approaches and evaluation frameworks, the challenges to adopting
LLM technologies in clinical research and exciting future directions.
