用大型语言模型进行临床文本总结的科学证据:范围审查
Lydie Bednarczyk1, Daniel Reichenpfader2,3, Christophe Gaudet-Blavignac1
1Division of Medical Information Sciences, University Hospital of Geneva, Geneva, Switzerland.
Journal of medical Internet research
|May 15, 2025
概括
大型语言模型对总结临床文本充满希望,但目前的研究范围和评估严格性有限. 对于值得信赖的临床应用,需要更强大的框架.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 临床医生面临来自电子健康记录的信息过载.
- 使用大型语言模型 (LLM) 进行自动总结是一个日益增长的研究领域.
- 需要对基于LLM的临床文本总结进行结构化的概述.
研究的目的:
- 通过LLMs.审查临床文本总结技术的现状.
- 评估现有研究的证据水平.
- 评估当前总结发现的临床适用性.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在5个数据库中搜索了2019年1月至2024年6月的文献.
- 包括基于变压器模型的研究,用于使用自由文本数据进行临床文本总结.
主要成果:
- 分析了30项研究,主要是使用真实患者数据的回顾性观察设计.
- 研究的重点很窄,通常是来自重症监护病房的放射学报告,主要是在美国.
- 总结方法主要是抽象的,报告不一致,评估框架异质;外部验证和安全分析很少.
结论:
- 目前的研究转化为临床实践存在重大障碍.
- 该领域是探索性的,范围有限,对性能和临床影响的评估不足.
- 进步需要更广泛的范围,强大的评估,并专注于现实世界的适用性,安全性和公平性.
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