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人工智能工具对PRISMA方法进行文献搜索,数据提取和研究组合的评估

Laura Antonia Meliante¹, Giulia Coco¹, Alessandro Rabiolo²

¹Department of Clinical Sciences and Translational Medicine, University of Rome Tor Vergata, Facoltà di Medicina e Chirurgia East Tower-6th Floor Via Montpellier 1, Rome, 00133, Italy, 39 3484772028.

|September 5, 2025

概括

人工智能 (AI) 工具在系统文献审查 (SLR) 中存在局限性,系统审查和元分析 (PRISMA) 首选报告项目方法在准确性和可重复性方面仍然优越. 研究人员的监督对于质量控制至关重要.

科学领域:

眼科眼科
医疗信息学
研究中的人工智能

背景情况:

人工智能 (AI) 在科学研究中越来越多地用于数据分析和手稿编制.
它在系统文献评论 (SLR) 中的应用是一个新兴的兴趣领域.
评估人工智能在单反相机中的有效性对于了解其潜力和局限性至关重要.

研究的目的:

研究人工智能在进行系统文献审查中的作用和局限性.
在关键的SLR阶段评估AI的性能:研究,研究选择,数据提取和构成.
将人工智能辅助的SLR流程与已建立的系统审查和元分析 (PRISMA) 方法的首选报告项目进行比较.

主要方法:

评估了四个人工智能平台 (Connected Papers,Elicit,ChatPDF和Jenni AI).
这些平台测试了它们能够复制基于PRISMA的四个与眼相关的SLR.
对研究,数据提取准确性和内容组成进行了评估.

主要成果:

人工智能工具没有完全复制PRISMA在文献搜索中的综合结果.
数据提取的准确性各不相同:Elicit的平均准确性为51.40%,ChatPDF的平均准确性为60.33%.
但缺乏方法和结果的深度.

结论:

PRISMA方法仍然是SLR准确性和可重复性的黄金标准.
人工智能工具可以帮助重复任务,但不能取代研究人员的关键监督.
保持研究人员的参与对于确保单反相机的质量,准确性和客观性至关重要.

关键词:

没有.系统审查中的人工智能人工智能辅助的学术写作人工智能辅助的数据分析聊天PDF 相关文件引发的詹妮·艾一个SLR 人工智能系统文献审查

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