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人在循环中的人工智能系统用于系统文献审查:AutoLit审查软件的方法和验证

Kevin M Kallmes¹, Jade Thurnham¹, Marius Sauca¹

¹Nested Knowledge St. Paul Minnesota USA.

Cochrane evidence synthesis and methods

|October 27, 2025

概括

本研究介绍了一种人工智能工具AutoLit,它支持人类监督的系统文献审查 (SLR) 的所有阶段. 人工智能工具在搜索,选和提取中显示了显著的时间节约和高准确性,以实现强大的证据合成.

科学领域:

科学研究中的人工智能
系统性文献审查方法论方法论
证据综合和元分析

背景情况:

系统性文献审查 (SLR) 对于证据综合至关重要,但耗时.
现有的AI工具只支持单个SLR阶段,而不是完整的工作流.
确保SLR发现的质量和准确性需要专家监督.

研究的目的:

提出一个全面的方法来进行SLR使用一个集成的人工智能工具与人-in-the-loop策划.
通过专家评价验证人工智能工具的性能,并量化节省时间.
概述在人工智能辅助的单反相机中保持最佳实践的方法.

主要方法:

使用了AutoLit软件,集成AI用于搜索策略生成,双选和证据提取.
集成的手动批判性评估和人工智能驱动的网络元分析.
进行了验证,将AI性能与人类专家进行比较,评估节省时间和"快速审查"替代方案.

主要成果:

人工智能驱动的搜索策略生成实现了76.8-79.6%的回忆.
对查的监督机器学习达到82-97%的回忆.
证据提取 (PICOs) 显示F1得分为0.74,研究类型,位置和大小准确率分别为74%,78%和91%.
在抽象选中节省了50%的时间,在定性提取中节省了70-80%.

结论:

人工智能系统,在人类监督下,可以有效地支持高质量的系统文献评论.
透明度,可复制性和专家参与是成功的人工智能辅助SLR的关键.
该AutoLit方法为高效可靠的证据综合提供了一个框架.

关键词:

人工智能的人工智能是人工智能.证据综合证据综合在循环中的人类.这是一个元分析.系统的文献审查系统的文献审查

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