与已发布的搜索策略相比,ChatGPT生成系统审查搜索策略的能力
John P Bourgeois1, Hannah Ellingson2
1Webster Family Library, Cummings School of Veterinary Medicine at Tufts University, North Grafton, Massachusetts, USA.
Medical reference services quarterly
|August 1, 2025
概括
大型语言模型 (LLM) 显示出改善系统审查中的文献搜索的前景. 聊天GPT生成的搜索策略与传统的Medline OVID策略进行了比较,揭示了AI在研究合成中的潜力.
科学领域:
- 卫生科学 图书馆 图书馆 图书馆
- 研究中的人工智能.
- 系统审查方法论 系统审查方法论
背景情况:
- 人工智能 (AI) 在健康科学图书馆有着悠久的历史.
- 可访问的大型语言模型 (LLM) 的出现为研究提供了新的机会.
- 法律法学可能会改变寻找系统评论的文献.
研究的目的:
- 将LLM生成的搜索策略与传统方法的有效性进行比较.
- 评估ChatGPT能够为系统审查创建有效的搜索策略的能力.
- 评估AI驱动搜索在识别相关文献方面的表现.
主要方法:
- 作为基准,使用了一份已公布的系统审查.
- 根据审查的目标,ChatGPT生成了三个不同的搜索策略.
- 已发表的和LLM生成的搜索策略在Medline中执行.
- 结果与评论作者认为重要的文章进行了比较.
主要成果:
- 搜索策略绩效的比较正在进行中.
- 最初的分析将确定被检索的文章的重叠和差异.
- 该研究旨在量化LLM生成搜索的有效性.
结论:
- 法律法规具有增强或取代传统搜索策略的潜力.
- 需要进一步的研究来验证LLMs在系统性审查中的使用.
- 由人工智能驱动的工具可以提高文献综合的效率和全面性.
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