人工智能和机器学习以提高证据合成生产效率:对资源使用和完成时间的观察性研究
Christopher James Rose1,2, Jose Francisco Meneses-Echavez1,3, Ashley Elizabeth Muller1,4
1Reviews and Health Technology Assessments, Division of Health Services Norwegian Institute of Public Health Oslo Norway.
Cochrane evidence synthesis and methods..
|July 14, 2025
概括
在本研究中,人工智能和机器学习 (AI/ML) 工具并没有显著影响证据综合所需的时间或资源. 需要进一步的研究来确定AI/ML在系统性审查中的真正效率增长.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 信息科学 信息科学 信息科学
背景情况:
- 综合证据至关重要,但耗时且资源密集.
- 人工智能和机器学习 (AI / ML) 在审查过程中提供了潜在的效率改进.
- 人工智能/ML对整体证据综合生产的影响在很大程度上是未知的.
研究的目的:
- 分析AI/ML工具对资源使用和证据综合的完成时间的影响.
- 根据内部建议,将使用AI/ML的评论与没有使用AI/ML的评论进行比较.
主要方法:
- 在2020年8月至2023年1月期间委托进行的与医疗保健或福利相关的审查的预规定的分析.
- 使用AI/ML工具 (排名,分类,聚类,文献计量) 的评论与没有使用AI/ML工具的评论进行了比较.
- 考虑到非随机分配和审查的结果;研究人员和统计学家被蒙蔽了.
主要成果:
- 在39项审查的研究中,AI/ML工具在69%使用.
- 据报道,使用AI/ML的评论使用了更多的资源 (667人 vs. 291人小时),并且完成的速度略快 (27.6人 vs. 28.2周).
- 这些资源使用和完成时间的差异在统计学上并不显著.
结论:
- 人工智能/ML使用与证据综合效率之间的关联仍然不确定.
- 可能需要进行更大规模的多中心研究或元分析来最终评估AI/ML工具的好处.
- 需要进一步调查,以确定AI/ML工具是否可以有意义地减少生产证据合成所需的时间和资源.
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