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在老年人中评估轻度认知障碍的大型语言模型:ChatGPT,Gemini和Kimi的双语比较

Yexuan Xiao^1,2,3, Qianhui Pan^2,3, Haoyuan Liu^2,3

¹Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.

Health informatics journal

|September 16, 2025

概括

大型语言模型 (LLM) 在管理轻度认知障碍 (MCI) 和帮助医疗保健专业人员方面表现有前途. 然而,英语回复比中文更有效,突出了对语言特定适应的需要.

科学领域:

医疗保健中的人工智能
临床决策支持系统临床决策支持系统
神经退行性疾病管理管理

背景情况:

轻度认知障碍 (MCI) 给医疗保健专业人员和护理合作伙伴带来了挑战.
有效的沟通和可访问的信息对于管理MCI至关重要.
大型语言模型 (LLM) 为信息传播和支持提供了潜在的工具.

研究的目的:

评估LLM (ChatGPT-4o,Gemini,Kimi) 在治疗MCI中的有效性.
为了比较英语与中文的LLM成绩.
评估对医疗保健专业人员和护理合作伙伴的LLM支持.

主要方法:

72个与MCI相关的问题被提出给三个LLM.
答案被评估为准确性,可理解性,具体性和可操作性,使用5分利克特级别.
统计分析包括类内相关系数和曼-惠特尼U测试.

主要成果:

在症状和诊断领域 (M = 4.11 ± 0.15) 中,LLM表现出色.
医疗保健专业人员比护理伙伴受益更多,特别是在可理解性和可操作性方面 (p < .001).
英语LLM的答案比中文的答案更容易理解和具体 (p < .001).

结论:

法律学士在支持MCI管理方面显示出潜力,特别是在诊断和可操作的见解方面.
跨语言和用户群体存在性能差异,英语通常优于中文.
在文化和语言方面量身定制的LLM对于优化MCI护理和提高可用性至关重要.

关键词:

MCI管理策略的管理策略.人工智能的人工智能是人工智能.双语评估双语评价双语评价大型语言模型.轻度的认知障碍轻度的认知障碍

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