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.

翻译：大语言模型的最新进展显著提升了通用领域自然语言处理任务的性能。然而，在临床试验招募等关键领域，其应用仍较为有限。由于试验方案以自然语言设计，患者数据则表现为结构化和非结构化文本，试验与患者的匹配任务可从大语言模型的知识整合与推理能力中获益。传统方法通常针对特定试验，而大语言模型凭借其整合分布式知识的能力，有望构建更通用的解决方案。然而，当前大语言模型辅助方法的应用多依赖专有模型和薄弱的评估基准。本综述首次系统分析了试验-患者匹配任务，并将新兴的基于大语言模型的方法置于临床试验招募的背景下进行审视。我们批判性地检视了现有基准、方法及评估框架，探讨了在临床研究中应用大语言模型技术面临的挑战，并展望了未来令人振奋的发展方向。