进行系统性审查的大型语言模型:正在增加,但尚未准备好使用-一个范围审查
Judith-Lisa Lieberum1, Markus Toews2, Maria-Inti Metzendorf3
1Eye Clinic, Medical Center - University of Freiburg/Medical Faculty - University of Freiburg, Freiburg, Germany.
Journal of clinical epidemiology
|February 28, 2025
概括
大型语言模型 (LLM) 显示出有助于系统审查 (SRs) 在健康研究中的承诺,特别是在文献搜索,研究选择和数据提取方面. 然而,尽管越来越重要,但仍需要完全验证的应用程序.
科学领域:
- 卫生研究 卫生研究 卫生研究
- 人工智能的人工智能
- 证据综合 证据综合
背景情况:
- 系统性审查 (SRs) 在健康研究中对于综合证据至关重要.
- 创建SR需要大量的时间和资源.
- 大型语言模型 (LLM) 提供了简化SR流程的潜力.
研究的目的:
- 提供在健康研究中的SR行为中LLM应用的概述.
- 确定在SR中LLM使用的范围和性质.
- 评估LLM在SRs中感知到的有效性.
主要方法:
- 在多个数据库 (MEDLINE,科学网等) 进行系统的文献搜索. 和手搜. 搜索. 在手搜.
- 从2021年4月起,包括英语/德语文章.
- 一个审核员提取数据,另一个审核员检查,重点关注LLM对SR步骤的支持.
主要成果:
- 包括37篇文章,重点关注LLM对SRs的支持.
- 在13个SR步骤中,LLM应用于10个,最常见的是文献搜索 (41%),研究选择 (38%) 和数据提取 (30%).
- 最常见的LLM是生成预训练变压器 (GPT) (89%);57%的研究是以验证为重点的,在LLM承诺方面有混合结果 (54%有希望,24%中立,22%不有希望).
结论:
- LLM 显示了提高SR效率的潜力.
- 目前对SR的完全建立和验证的LLM应用程序是有限的.
- 研究的快速增长凸显了LLMs在证据综合中的越来越重要.
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