mlsanityData to 14 Oct 2026

A Survey on LLM-Assisted Clinical Trial Recruitment

arXiv 2506.15301 ↗First posted 18 Jun 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 131 references that are in the map. First posted on arXiv 18 Jun 2025.

Builds on

35 of 131 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Studycited by 3
  2. 2026Neurosymbolic Clinical Trial Matching via LLM-Driven Abduction and Logical Verificationcited by 0

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  2. 2026Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives
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  6. 2025Developing Large Language Models for Clinical Research Using One Million Clinical Trials
  7. 2025Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media
  8. 2026A primer on evaluation methods for large language models in healthcare