加强AI用于文献评论中的引文选:通过集合模型提高准确性
Zhihong Zhang1, Mohamad Javad Momeni Nezhad2, Pallavi Gupta3
1Data Science Institute, Columbia University, New York, NY 10027, USA; School of Nursing, Columbia University, New York, NY 10032, USA.
International journal of medical informatics
|July 3, 2025
概括
使用大型语言模型 (LLM) 的集合方法显著改善了医疗保健证据综合的引用选. 这些自动化方法提高了研究的效率和可扩展性,有助于临床决策.
科学领域:
- 医疗保健中的人工智能
- 证据综合和研究方法研究方法.
背景情况:
- 医疗保健文献审查对于基于证据的实践和临床指南至关重要.
- 引用选是证据综合的重要但耗时的组成部分.
- 本研究研究了使用大型语言模型 (LLM) 来自动化引用选.
研究的目的:
- 评估个人LLM与集体方法对自动引用选的有效性.
- 提高医疗保健研究中证据综合的效率和可扩展性.
- 根据灵敏度和特异性,比较各种LLM和组合技术的性能.
主要方法:
- 评估了三次医疗保健审查的绩效,范围和纳入率各不相同.
- 评估了6个LLM (GPT-4o Mini,GPT-4o,Gemini Flash,Llama 3.1变体) 使用零和少射击学习.
- 用PubMedBERT进行演示选择,比较个人LLM与整体方法 (多数投票,随机森林) 的表现.
主要成果:
- 没有单一的LLM始终优于其他;绩效因审查范围和纳入标准而有所不同.
- 在所有评估的审查中,整体方法在所有评估的审查中始终优于单个LLM.
- 特定组合配置实现了高灵敏度和特异性,其中一些在一次审查中达到完美的分类.
结论:
- 合并方法提高了在各种医疗保健审查中引用选的LLM绩效.
- 通过集体自动引用选可以改善证据合成工作流程.
- 在广泛临床实施之前,需要进一步验证.
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