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PICOT问题和搜索策略的制定:一种使用人工智能自动化技术的新方法
Lucija Gosak1, Gregor Štiglic1,2,3, Lisiane Pruinelli4
1Faculty of Health Sciences, University of Maribor, Maribor, Slovenia.
概括
人工智能 (AI) 大型语言模型 (LLM) 可以帮助开发临床问题和搜索策略. 虽然ChatGPT-3.5和Bing显示出潜力,但人类监督对于临床工作流程的最佳结果至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到医疗保健工作流中.
- 评估像ChatGPT-3.5,Bing和Bard这样的AI工具在生成临床查询中的有效性至关重要.
- 开发有效的临床搜索策略,如PICOT (人口,干预,比较,结果,时间) 问题,对于基于证据的实践至关重要.
研究的目的:
- 为了比较基于AI的LLM (ChatGPT-3.5,Bing,Bard) 与人类专家在制定临床问题和搜索策略方面的表现.
- 评估不同LLM和人类专家生成的搜索结果的相关性和质量.
- 评估AI在增强临床工作流程和改善医疗保健专业人员信息检索方面的潜力.
主要方法:
- 开发了脚本和提示,以生成PICOT问题和使用ChatGPT-3.5,Bing Chat和Google Bard的搜索策略.
- 用LLM生成的搜索字符串来检索PubMed,科学网,科克兰图书馆和CINAHL Ultimate的结果.
- 人类专家评估了搜索结果的相关性,并使用描述性和独立的评估来评估LLM答案的质量. 计算了克伦巴赫的α和类内相关系数 (ICC).
主要成果:
- 人类专家产生了5919次访问,65.22%的相关性,而人工智能模型产生了不同的结果:Bing (1,376,854次访问,70.79%的相关性),Bard (16,583次访问,13.29%的相关性) 和ChatGPT-3.5 (11,859次访问,21.05%的相关性).
- 聊天GPT-3.5获得了评价员的最高质量评分,优先考虑上下文准确性而不是纯粹的相关性.
- 尽管相关性百分比较低,但ChatGPT-3.5显示出对细微的临床查询配方的潜力,这得到了高的评价者间可靠性的支持.
结论:
- 基于AI的LLM在帮助医疗保健专业人员制定临床问题和开发搜索策略方面显示出显著的前景.
- 聊天GPT-3.5和Bing展示了增强临床工作流程的潜力,尽管它们的性能在相关性和结果数量方面有所不同.
- 持续的人类监督和人工智能模型的进一步改进是必要的,以最大限度地提高它们在临床实践和信息检索中的好处.
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