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神经学实践中的大型语言模型:现实世界的研究

Natale Vincenzo Maiorana¹, Sara Marceglia¹, Mauro Treddenti^1,2

¹Aldo Ravelli Center for Neurotechnology and Experimental Brain Therapeutics, Department of Health Sciences, University of Milan, Via Antonio di Rudinì, 8, Milan, 20142, Italy, 39 02 50323233.

Journal of medical Internet research

|September 22, 2025

概括

大型语言模型 (LLM) 在神经学中显示出潜力,但尚未可靠用于独立诊断. 神经学家的表现优于人工智能工具,突出了在临床环境中进一步验证人工智能的必要性.

科学领域:

医学诊断医学诊断医学诊断
医疗保健中的人工智能
神经学神经学

背景情况:

像ChatGPT和Gemini这样的大型语言模型 (LLM) 正在被探索用于医学诊断应用,特别是在神经学中.
这些自由可用的LLM在临床工作流程中的现实应用性和诊断准确性仍未得到充分评估.

研究的目的:

与神经病学家相比,评估未经培训的,免费可用的LLM (ChatGPT和Gemini) 的诊断准确性.
评估LLM在现实神经病例中提出的临床建议和诊断测试建议的适当性.

主要方法:

一项实验性评估,比较LLM诊断性能与临床神经病学家,使用28个匿名的真实世界神经病例.
基于来自医院神经病学部门的患者记录,模拟的第一次访问场景.
主要结果:诊断准确性 (与出院诊断一致);次要结果:测试建议的适当性,评价者之间的协议和提示需求.

主要成果:

神经学家实现了75%的诊断准确度,超过了ChatGPT (54%) 和双子座 (46%).
终身学士在细微推理和过度推的诊断测试方面表现出局限性 (17%-25%的病例).
复杂的病例需要对LLM进行额外的提示,在人类评级者中观察到中度的协议 (κ=0.47).

结论:

自由可用的,未经培训的LLM目前缺乏在神经病学中独立临床决策所需的深度.
该研究强调,需要在将AI工具集成到临床工作流程之前进行严格的验证.
未来的研究应该专注于提高LLM能力,并为现实世界的神经学实践开发强大的评估方法.

关键词:

聊天GPT 聊天在GPT 聊天双子座是一个双子座.人工智能的人工智能是人工智能.临床实践中的临床实践大型语言模型神经学神经学神经学

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