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评估大型语言模型在与皮症相关的患者查询中的性能:为以患者为中心的验证进行基础评估

Tao Huang¹, Ben Kirk², Jacqueline Close³

¹Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong SAR, China.

Frontiers in aging

|March 16, 2026

概括

大型语言模型 (LLM) 对皮病信息有希望,所有测试模型都显示出良好的准确性和全面性. 专家评估表明,LLM可以帮助患者教育,但人类监督对于安全实施至关重要.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
老年病的医生老年病的医生

背景情况:

大型语言模型 (LLM) 在临床环境中显示出潜力.
在萨尔科佩尼亚等专业领域的LLM绩效被低估了.

研究的目的:

评估LLM对以患者为中心的萨尔科佩尼亚查询的答案的准确性和全面性.
为了比较三个领先的LLM在提供肉症信息方面的表现.

主要方法:

一个由sarcopenia专家组成的小组制定了20个标准化问题.
三个LLM (Deepseek,ChatGPT,Gemini) 回答了这些问题.
答案是匿名的,并由三个独立的临床研究人员对准确性和全面性进行评估.

主要成果:

所有LLM的表现都很好,没有"差"准确度评级.
深度搜索提供了最详细的答案,在"风险因素"和"预后"方面表现出色.
聊天GPT提供了简洁的答案,最高比例的"好"评级;双子座在"发病"和"诊断"方面领先.

结论:

所有评估的LLM都表现出了可接受的准确性和全面性,对于与肉类相关的查询.
LLM可以作为宝贵的工具,用于患者教育和护理在肉症.
人类监督对于安全有效的临床应用至关重要.

关键词:

聊天GPT 聊天在GPT 聊天双子座是一个双子座.在深度搜索中,深度搜索.大型语言模型.这种类型的麻症是sarcopenia.

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