PubMed及其他领域:在人工智能时代的生物医学文献搜索
Qiao Jin1, Robert Leaman1, Zhiyong Lu1
1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
EBioMedicine
|February 2, 2024
概括
这项研究回顾了30多个AI驱动的生物医学研究文献搜索工具. 它帮助临床医生和研究人员使用高级搜索功能高效地找到相关信息.
科学领域:
- 生物医学信息学 生物医学信息学
- 医学文献搜索 医学文献搜索
- 医疗保健中的人工智能
背景情况:
- 生物医学研究产生了大量的文献,使得文献搜索对医疗保健专业人员和研究人员至关重要.
- 传统的基于关键字的搜索有局限性,人工智能 (AI) 最近的进步提供了增强的功能.
- 临床医生和研究人员可能会发现新的AI驱动工具不熟悉,需要指导.
研究的目的:
- 为生物医学用例提供30多个文献搜索工具的概述.
- 通过有效的文献搜索,帮助读者有效地满足他们的信息需求.
- 探索AI在生物医学文献发现中的不断发展的景观.
主要方法:
- 对30多个文献搜索工具进行审查和分类.
- 讨论PubMed最近的改进和挑战.
- 针对特定需求的基于人工智能的工具描述:基于证据的医学,精准医学/基因组学,语义搜索,文章推和文献挖掘.
- 分析像ChatGPT这样的大型语言模型 (LLM) 的影响.
主要成果:
- 在AI进步的背景下,PubMed的优势和局限性.
- 人工智能工具的分类,解决五个关键的生物医学信息需求.
- 确定基于证据的医学,基因组学,语义搜索,相关文章和数据挖掘的工具.
- 探索LLM如何改变生物医学信息搜索.
结论:
- 人工智能为超越传统关键字的生物医学文献搜索提供了强大的新方法.
- 精心挑选的工具可以显著提高临床医生和研究人员的效率.
- 了解和利用这些人工智能工具,包括LLM,对于保持生物医学的最新状态至关重要.
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