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Yangyi Huang¹, Runhan Shi², Can Chen¹

¹Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Research Center of Ophthalmology and Optometry, China; Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care, China.

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概括

大型语言模型 (LLM) 在教育患者关于关键的近视控制方法 - - 脊椎病理学方面表现有前途. Qwen-72B和GPT-4提供了准确的信息,尽管需要进一步的语言改进.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医疗教育医学教育

背景情况:

大型语言模型 (LLM) 越来越多地用于眼科应用.
在对骨髓瘤学 (一种主要的近视控制策略) 的患者教育中,LLM的有效性仍未得到评估.

研究的目的:

评估不同大型语言模型 (LLM) 在提供患者对脊椎病理学教育方面的有效性.
为了比较GPT-4,Qwen-72B和Yi-34B在响应质量,准确性和用户满意度方面对 ортокератология查询进行比较.

主要方法:

一项横截面研究使用了一个由24个GPT-4,Qwen-72B和Yi-34B的orthokeratology查询组成的问题库.
眼科医生和近视儿童的父母使用5分利克尔特尺度进行了客观和主观评估.
评估的指标包括正确性,相关性,可读性,适用性,安全性,清晰性,帮助性和整体满意度.

主要成果:

在LLMs中没有发现安全性,相关性,正确性或适用性的显著差异.
与Yi-34B和Qwen-72B相比,GPT-4的可读性得分较低.
父母对所有LLM的评分都很高 (平均值>4.7),GPT-4在帮助和满意度方面表现出色,而Qwen-72B在整体标准化得分方面取得了更高的成绩.

结论:

GPT-4和Qwen-72B提供了准确和有用的响应,用于骨关节炎患者教育.
持续改进是必要的,以提高响应的准确性,特别是在不同的语言和环境.

关键词:

人工智能的人工智能是人工智能.大型语言模型.近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近视近 ортокератология ортокератология是一个专业的专业.患者教育患者教育

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