在证据合成中使用生成性人工智能:系统性审查
Justin Clark1, Belinda Barton2, Loai Albarqouni1
1Institute for Evidence-Based Healthcare, Bond University, Gold Coast, QLD, Australia.
Research synthesis methods
|February 2, 2026
概括
生成型人工智能 (GenAI) 在搜索和选等证据合成任务中显示出显著的不准确性. 目前的证据不支持在没有人类监督的情况下使用它,尽管它可能有助于执行特定任务.
科学领域:
- 医疗信息学 医疗信息学
- 研究中的人工智能.
- 证据综合的方法论.
背景情况:
- 生成型人工智能 (GenAI) 工具越来越容易用于研究.
- 基因人工智能被提出作为一种加速证据合成的工具.
- 本研究评估了与传统方法相比,GenAI在证据综合方面的表现.
研究的目的:
- 评估GenAI在证据合成中的准确性,错误率和节省时间的潜力.
- 将GenAI的表现与专家驱动的证据综合方法进行比较.
主要方法:
- 在2025年1月17日,通过搜索五个数据库进行了系统审查.
- 结果包括准确性,错误率和GenAI在证据合成中所花费的时间.
- 使用修改后的 QUADAS-2 工具评估了偏差风险.
主要成果:
- 在搜索过程中,GenAI错过了68% - 96%的研究.
- 错误的选决策范围为包括的0%-29%和排除的1%-83%.
- 还观察到数据提取错误 (4%-31%) 和偏差风险评估错误 (10%-56%).
结论:
- 目前的证据不支持在证据合成中使用无监督的GenAI.
- 基因人工智能可以协助人类进行证据合成,用于除初始搜索之外的其他任务.
- 人类监督对于可靠的GenAI辅助证据合成至关重要.
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