利用聊天GPT-4进行证据合成:在系统性审查中使用大型语言模型的案例研究
Federica Tomassini1, Alice Luraschi1, Stefano Patarnello1
1Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Studies in health technology and informatics
|October 3, 2025
概括
人工智能 (AI),特别是大型语言模型 (LLM),可以显著加快系统文献审查 (SLR). 虽然人工智能优化了许多任务,但人类的专业知识仍然对复杂的分析和人工智能辅助单反相机的最终起草至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 证据综合 证据综合
背景情况:
- 系统性文献评论 (SLR) 是必不可少的,但需要大量的时间.
- 大型语言模型 (LLM) 显示了简化研究流程的潜力.
- 评估AI对SLR方法的影响对于采用至关重要.
研究的目的:
- 使用ChatGPT,将传统单反相机与人工智能辅助单反相机的效率进行比较.
- 量化SLR过程不同阶段的时间节约.
- 在复杂的研究任务中识别AI的局限性.
主要方法:
- 与人工智能辅助的单反相机 (ChatGPT) 进行传统的Cochrane引导单反相机的比较.
- 在每个SLR阶段对时间支出的定量评估.
- 评估人工智能在任务中的表现,从问题制定到撰写报告.
主要成果:
- 在生成搜索术语,选择标准和甘特图中观察到显著的时间缩短.
- 在AI执行需要解释性判断的任务的能力中发现的挑战,例如结果分析和偏见风险评估.
- 人工智能在最后的起草阶段表现出局限性.
结论:
- 人工智能,特别是像ChatGPT这样的LLM,可以通过减少操作任务的时间来优化SLR工作流程.
- 人类研究人员对于批判性解释,偏见评估和最终报告组成至关重要.
- 结合人类专业知识和人工智能能力的混合方法为高效可靠的单反相机提供了一个有希望的未来,这取决于人工智能系统的改进.
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