由大型语言模型驱动的聊天机器人的能力,以支持范围审查的选过程:可行性研究
Kim Nordmann1, Michael Schaller1, Stefanie Sauter1
1Bavarian Research Center for Digital Health and Social Care, Kempten University of Applied Sciences, Kempten 87437, Germany.
JAMIA open
|January 12, 2026
概括
与Rayyan相比,ChatGPT 4.0显著提高了范围审查选效率,显示出高精度和工作负载节省. 人类监督对于验证人工智能驱动的文献评估流程至关重要.
科学领域:
- 信息科学 信息科学 信息科学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 越来越多的科学出版物需要有效的方法来进行文献评论.
- 标题和摘要选是系统和范围审查中的一个主要瓶.
- 现有的半自动化工具在范围审查选方面表现出有限的成功.
研究的目的:
- 评估ChatGPT 4.0在标题和摘要选中的性能,以进行范围审查.
- 将ChatGPT 4.0的效率和准确性与半自动化工具Rayyan进行比较.
- 评估大型语言模型在简化文献评估方面的潜力.
主要方法:
- 一项回顾性研究使用了对15307份摘要的人类选决策作为基准.
- 对ChatGPT的快速工程和对Rayyan的培训是在100个抽象集上进行的.
- 性能指标 (准确度,灵敏度,特异性) 使用 Stata. 进行计算.
主要成果:
- 聊天GPT 4.0 选了 15,306 个摘要,表现优于 Rayyan.
- 聊天GPT 4.0实现了68%的准确性,67%的特异性和88%-89%的灵敏性.
- 观察到99%的负预测值和11%的假负率,节省了64%的工作负载.
结论:
- 聊天GPT显示了提高范围审查选的初始阶段的巨大潜力.
- 人类监督对于验证人工智能辅助的文献评估至关重要.
- 需要进一步的研究来标准化AI选工具的提示和参数.
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