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对大型语言模型进行比较分析,为患者提供有关角和隐形眼镜的信息

Yavuz Kemal Aribas¹, Atike Burcin Tefon Aribas²

¹Department of Ophthalmology, Hacettepe University Medical School, 06230, Ankara, Turkey. ykemal89@hotmail.com.

International ophthalmology

|August 18, 2025

概括

像ChatGPT,Gemini和Copilot这样的大型语言模型 (LLM) 通常提供关于角膜和隐形眼镜的准确信息. 然而,它们的可读性很差,需要临床医生监督患者教育.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

形眼镜的管理和隐形眼镜的安装需要准确的患者信息.
大型语言模型 (LLM) 越来越多地用于健康信息检索.
需要对LLM产生的健康内容的准确性和质量进行评估.

研究的目的:

评估来自ChatGPT,Gemini和Copilot的回复的准确性,完整性,信息质量和可读性.
为了评估LLM在患者特定的问题上的表现,关于角膜和隐形眼镜的使用.

主要方法:

一项横截面研究使用了32个患者关于角膜和隐形眼镜的问题.
三个LLM (ChatGPT,Gemini,Copilot) 使用他们的免费版本进行了查询.
眼科医生评价了准确性和完整性;DISCERN评价了信息质量;FRES和FKGL评价了可读性.

主要成果:

与Gemini和Copilot相比,ChatGPT表现出更高的准确性和完整性.
在DISCERN上,ChatGPT,Copilot和Gemini分别获得了64分,61分和55分.
所有的LLM都产生了低可读性 (FRES<50) 和高FKGL (高中后期水平) 的内容.

结论:

临床眼镜可以提供关于角膜和隐形眼镜使用的一般准确和彻底的信息.
可读性限制需要临床医生的监督,以确保患者的理解.
定制LLM产生的信息对于有效的患者教育至关重要.

关键词:

人工智能的人工智能是人工智能.聊天GPT 聊天GPT 聊天隐形眼镜的隐形眼镜是一个隐形眼镜.健康信息的质量和质量.克拉托科努斯 (Keratoconus) 是一种形状的形状.大型语言模型 (LLM)

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