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在神经学板式问题上评估多个生成性大语言模型

Mohammad Almomani¹, Vijaya Valaparla¹, James Weatherhead²

¹Department of Neurology, University of Texas Medical Branch (UTMB), Galveston, TX, United States.

Frontiers in digital health

|January 21, 2026

概括

像ChatGPT-5和ChatGPT-4o这样的新大型语言模型 (LLM) 在板式问题上优于神经病学居民. 虽然双子座2.5改进了,但LLM需要仔细整合作为教育工具.

科学领域:

人工智能在医学中的应用
神经学教育技术技术神经学教育技术

背景情况:

大型语言模型 (LLM) 在医学教育中的整合正在迅速发展.
与人类专家相比,评估LLM的表现对于了解其潜力和局限性至关重要.

研究的目的:

为了比较八个LLM与神经病学住院医生的表现,在董事会式的多选择题.
评估七个神经学子专业和两个认知水平 (布鲁姆分类) 的LLM绩效.

主要方法:

一项横截面比较研究评估了Bard,Claude,Gemini (v1,2.5) 和ChatGPT (3.5,4,4o,5) 在107个仅文本的神经学问题上.
问题由神经病学家根据认知水平进行分类;模型和居民在受控条件下回答项目.
结果包括准确性,信心准确性校准和代际扩展.

主要成果:

聊天GPT-5 (84.1%) 和聊天GPT-4o (81.3%) 显著超过了居民 (64.9%).
双子座2.5 (77.6%) 也超过了居民,比双子座v1 (39.3%) 显著改善.
在所有模型中,信心精度校准都很弱,代际缩放有所不同.

结论:

高级LLM,特别是ChatGPT-5和ChatGPT-4o,在神经学板式问题上表现优异,与居民相比.
双子座2.5显示显著的收益,但与不均的域扩展.
由于信心校准较弱,LLM应作为受监督的教育辅助程序使用,并进行持续验证.

关键词:

人工智能的人工智能是人工智能.委员会考试委员会考试.大型语言模型.模型性能分析分析模型性能分析神经学教育神经学教育

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