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生物医学研究中的人工智能工具:第1部分-文学搜索和知识挖掘

Chandan K Sen¹

¹Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

Antioxidants & redox signaling

|December 23, 2025

概括

人工智能 (AI) 工具通过增强文献发现和知识挖掘来彻底改变生物医学研究. 在提供强大的能力的同时,研究人员必须应对输出变化等挑战,并确保专家监督以获得准确的见解.

科学领域:

生物医学研究生物医学研究
人工智能的人工智能
科学文献分析科学文献分析

背景情况:

生物医学文献的指数增长需要先进的搜索和知识挖掘解决方案.
传统的搜索方法越来越不足以管理大量的科学数据.
人工智能 (AI) 对生物医学研究工作流程具有变革性的潜力.

研究的目的:

为生物医学研究中人工智能进步,应用和挑战提供全面的分析.
综合AI工具的演变,功能和局限性,用于文学发现,总结和证据合成.
为研究人员将AI整合到他们的工作流程中提供可操作的见解.

主要方法:

对人工智能的最新进展进行分析,以进行文献搜索.
评估人工智能工具的实际应用,如Elicit,BioGPT,PubTator 3.0,ResearchRabbit和Connected Papers等. 这些工具包括:
系统审查工具的审查 (Rayyan,Covidence) 和人工智能部署的挑战.

主要成果:

人工智能工具已经从基于关键字的演变为语义和多模式的方法.
特定平台在提取基因疾病关联,可视化网络和减少系统审查查时间方面表现出能力.
挑战包括输出质量的变化,幻觉风险和缺乏算法透明度.

结论:

由人工智能驱动的文献工作流可以加速假设生成,系统审查和翻译研究.
开源和可解释性的AI工具为当前的挑战提供了解决方案.
人类专家的监督对于确保人工智能辅助研究的严谨性和准确性至关重要.

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