开发和评估使用ChatGPT作为护士进行结构化文献评论的选工具:概念验证研究结果
Alexandra Mudd1,2, Tiffany Conroy1, Siri Lygum Voldbjerg3,4,5
1Flinders University, Adelaide, Australia.
Journal of clinical nursing
|June 17, 2025
概括
大型语言模型 (LLM) 显示了对选研究摘要的希望,可能加速护士的文献审查. 聊天GPT-3.5 Turbo是最有效的,证明了基于证据的实践的可行性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 护理研究 护理研究
背景情况:
- 结构化的文献评论对于护理的基于证据的实践至关重要.
- 对研究摘要的有效选是文献评论中耗时的瓶.
- 大型语言模型 (LLM) 为简化研究流程提供了潜在的解决方案.
研究的目的:
- 评估使用LLM作为结构化文献评论中的选工具的可行性.
- 评估不同版本的ChatGPT在护理研究中抽象查的有效性.
- 通过提高审查效率来确定LLM是否可以促进基于证据的实践.
主要方法:
- 一项概念验证研究,采用使用ChatGPT和计算机编码的新抽象选方法.
- 在四个数据库中对400个与公众参与护理教育相关的摘要进行实证测试.
- 将ChatGPT查结果 (四个版本) 与人类护理研究人员进行比较,以CONSORT 2010检查清单为指导.
主要成果:
- 聊天GPT-3.5 Turbo在快速查方面表现出最高的有效性,假阴性率低于人类研究人员.
- 更先进的版本 (ChatGPT-4, 4 Turbo, 4 omni) 的效果较差,错误阴性率较高,并且在细微的护理概念上扎.
- 该研究强调了不同LLM版本在复杂的研究环境中的不同性能.
结论:
- 在不影响审查质量的情况下,LLM可以显著减少护士花费查摘要的时间.
- 法学士具有加速研究综合的潜力,弥合了研究与实践之间的差距.
- 拥有LLM的护士积极参与是利用其对复杂护理问题和基于证据的实践的好处至关重要的.
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