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大型语言模型在生成患者教育材料中的使用:一个范围审查

Alhasan AlSammarraie¹, Mowafa Househ¹

¹Faculty College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar1.

Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH

|April 14, 2025

概括

大型语言模型 (LLM) 显示出产生患者教育材料的前景,ChatGPT-4通常提供最准确的结果. 然而,很少有研究探讨提取增强生成或非英语应用.

科学领域:

医疗信息学医疗信息学
自然语言处理自然语言处理.
医学教育医学教育

背景情况:

患者教育利用基于证据的医疗信息,使个人能够管理自己的健康.
大型语言模型 (LLM) 是先进的NLP工具,能够生成类似人类的文本,包括患者教育材料.
将LLM纳入医疗保健提供了创造可访问和信息化的患者资源的潜力.

研究的目的:

系统地审查和绘制现有的关于LLM应用的文献,以生成患者教育材料.
确定与LLM在患者教育材料创建中的使用相关的关键主题和变量.
了解当前的环境,并识别研究中的差距.

主要方法:

根据JBI的指导方针进行了范围审查,搜索了五个数据库.
在选择相关研究时,采用了包含/排除标准.
在数据提取过程中使用了一种以检索增强生成 (RAG) 为灵感的框架,然后进行手动验证.

主要成果:

审查包括了69项研究,其中美国的研究贡献最多.
聊天GPT-4,聊天GPT-3.5,和巴德是最常被研究的法学士.
生成材料的准确性和可读性是主要评估指标,ChatGPT-4显示出卓越的准确性.

结论:

现有的LLM,特别是ChatGPT-4,在生成患者教育材料方面表现出很高的效率.
目前的研究主要集中在准确性和可读性上,RAG架构或多语言应用的探索有限.
需要进一步的研究来探索先进的LLM应用程序和患者教育的非英语语境.

关键词:

人工智能的人工智能巴尔德巴尔德巴尔德巴尔德是什么意思聊天GPT 聊天在GPT 聊天克劳德·克劳德·克劳德 (Claude Claude) 是一个副驾驶员是我们的副驾驶员.在DeepSeek搜索中深入搜索.双子座是一个双子座.生成性AI是一种人工智能.大型语言模型,变压器自然语言处理器自然语言处理器患者教育材料患者教育材料提示提示提示提示提示检索增强生成增强生成

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