综述大模型如何提升临床试验患者匹配效率
A Survey on LLM-Assisted Clinical Trial Recruitment
- 分析大模型在临床试验匹配中的知识整合与推理能力
- 指出当前方法依赖私有模型且评估基准薄弱
- 适合医疗AI研究者和临床研究管理者阅读
大模型在通用自然语言处理任务中取得显著进展，但在临床试验招募等关键领域应用仍有限。由于试验方案以自然语言描述，患者数据包含结构化与非结构化文本，利用大模型的知识聚合与推理能力可提升患者与试验的匹配效果。传统方法多为特定试验设计，而大模型具备整合分散知识的能力，有望构建更通用的解决方案。然而现有基于大模型的方法多依赖私有模型，且缺乏强有力的评估基准。本文首次系统分析试验-患者匹配任务，梳理新兴的大模型应用方法，批判性审视现有评估基准、技术路径与框架，揭示大模型在临床研究中应用的挑战与未来方向。
原文摘要 · Abstract (English)
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.
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