PubMed and Beyond: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, National Library of Medicine, National Institutes of Health.
ArXiv
|October 1, 2025
概括
这项调查审查了36个生物医学文献搜索工具,包括PubMed和AI驱动的选项. 它指导研究人员有效地找到高质量的临床研究,基因信息和概念关联.
科学领域:
- 生物医学信息学 生物医学信息学
- 医学研究 医学研究
- 信息科学 信息科学 信息科学
背景情况:
- 生物医学研究产生了大量的信息,需要进行文献搜索以获得知识的进步.
- 人工智能已经增强了关键字以外的搜索,但这些工具可能对用户来说并不熟悉.
- 有效的信息检索对于临床和生物医学研究至关重要.
研究的目的:
- 调查生物医学文献搜索工具,以满足一般和特定信息需求.
- 帮助临床医生和研究人员有效地满足他们的信息要求.
- 提供36个可用的工具和未来趋势的全面概述.
主要方法:
- 检查了PubMed的功能和挑战.
- 描述了针对特定需求的工具:基于证据的医学,基因组学,语义搜索,文章推和知识发现.
- 涵盖了工具选择和使用的实际考虑和最佳实践.
- 包括对未来搜索引擎的观点,包括大型语言模型.
主要成果:
- 识别和分类36个公开可用的生物医学文献搜索工具.
- 基于证据的医学,精准医学,语义搜索,文献推和概念关联挖掘的详细功能.
- 提供了PubMed的能力和局限性的见解.
结论:
- 介绍了36个生物医学文献搜索工具的综合调查.
- 该调查帮助用户选择适合各种信息需求的工具.
- 未来的文献搜索很可能将集成先进的AI和大型语言模型.
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