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Jamila Tukur Jido¹, Ahmed Al-Wizni², Mariam Alkateb³

¹Department of Geriatrics, Queen Elizabeth University Hospital, Glasgow, GBR.

|August 18, 2025

概括

大型语言模型 (LLM) 生成的健康信息比国家卫生服务 (NHS) 材料难以阅读. 确保人工智能生成的内容符合可读性标准对于公平的患者护理至关重要.

科学领域:

医疗保健通讯医疗保健通讯
人工智能在医学中的应用
患者教育患者教育

背景情况:

国家卫生服务 (NHS) 长期计划促进了可访问的患者信息的数字工具.
人工智能产生的健康材料的可读性,特别是对于口腔医学条件的可读性,尚未得到很好的证实.
常见的口服药物疾病包括异口症,口腔候群病和石病.

研究的目的:

通过三个大型语言模型 (LLM) 生成的患者信息传单的可读性与现有的NHS英国传单进行比较.
评估LLM产生的内容是否适合于口腔医学中的公共卫生传播.
评估人工智能在可访问,以患者为中心的医疗保健沟通中的作用.

主要方法:

对LLM生成的患者传单进行横截面分析,这些传单涉及异口症,口腔候群病和石病.
将LLM输出与相同的NHS英国患者传单进行比较.
使用Flesch阅读易度得分 (FRES),Flesch-Kincaid等级水平 (FKGL) 和枪击雾指数评估可读性.

主要成果:

英国NHS的传单显示出优异的可读性 (FKGL:5.9-6.3;FRES:70.5-72.4),适合11-14岁的人.
在LLM中,ChatGPT输出是最易读的 (FKGL:6.8-7.2).
深度搜索 (FKGL: 8.3-8.7) 和双子座 (FKGL: 9.7-10.2) 产生了越来越复杂的文本,经常超过推水平.

结论:

虽然LLM生成的患者信息很有希望,但比专业撰写的NHS材料要难以阅读.
在人工智能产生的内容中读数更高的水平可能会阻碍患者的理解,并加剧健康不平等.
确保人工智能产生的健康材料符合可读性标准对于公平,以患者为中心的护理至关重要.

关键词:

人工智能的人工智能是人工智能.提高健康素养提高健康素养大型语言模型.国家卫生服务国家卫生服务.根据NHS的指导方针患者教育患者教育可读性措施可读性措施

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