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通过大型语言模型加速临床证据合成.

Zifeng Wang1,2, Lang Cao1, Benjamin Danek1,2

  • 1Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA.

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概括

TrialMind是一个人工智能 (AI) 管道,通过改善系统审查 (SR) 中的研究搜索,选和数据提取来加速临床证据合成. 人类-人工智能与TrialMind的合作显著提高了效率和准确性.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 临床研究方法论 临床研究方法论

背景情况:

  • 系统性审查 (SR) 对于临床证据综合至关重要,但耗时.
  • 目前用于SR研究的研究识别,选和数据提取的方法面临效率挑战.

研究的目的:

  • 介绍TrialMind,一个生成AI管道,旨在自动化和增强SR的关键任务.
  • 与人类基线和现有的AI模型相比,评估TrialMind在研究搜索,选和数据提取方面的表现.

主要方法:

  • 使用已发表的SR和临床研究开发TrialMind AI管道.
  • 创建了TrialReviewBench数据集,包括100个SR和2,220个临床研究.
  • 对TrialMind与人类性能和GPT-4在搜索,选和数据提取任务中的性能进行比较分析.

主要成果:

  • 在研究搜索中,TrialMind实现了高回忆率 (0.711-0.834),显著超过了人类基线 (0.138-0.232).
  • 在研究查方面,TrialMind显示了1.5-2.6倍的改进,在数据提取准确度方面,GPT-4的表现超过了16-32%.
  • 使用TrialMind的人类-AI协作导致回忆率增加71.4%,选时间减少44.2%,数据提取精度提高了23.5%,时间减少了63.4%.

结论:

  • 在加速临床证据合成方面,TrialMind显示出显著的前景.
  • 人类-人工智能与TrialMind的合作提高了系统审查的效率和准确性.
  • 医学专家赞成TrialMind合成的证据,表明它有可能提供可靠的临床决策支持.