医学诊断中大型语言模型的语言依赖性表现:ChatGPT-4o和Claude 3.5索内特的比较研究
El Habib El Mahdaoui1, Yaroslav O Mykhalko2
1UNIVERTITE DE MONTREAL, MONTREAL, CANADA; UZHHOROD NATIONAL UNIVERSITY, UZHHOROD, UKRAINE.
Polski merkuriusz lekarski : organ Polskiego Towarzystwa Lekarskiego
|January 19, 2026
概括
像ChatGPT-4o和Claude 3.5 Sonnet这样的大型语言模型显示出类似的诊断准确性,性能受到语言,特别是阿拉伯语的严重影响. 需要进一步的研究来改善AI在多语言医疗保健环境.
科学领域:
- 人工智能在医学中的应用
- 在医疗保健中的自然语言处理.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医学应用.
- 在不同语言的疾病诊断中评估LLM绩效对于全球医疗保健公平至关重要.
- 之前的研究并没有在多语言诊断任务中全面比较像ChatGPT-4o和Claude 3.5 Sonnet这样的领先的LLM.
研究的目的:
- 通过临床病例描述,评估和比较ChatGPT-4o和Claude 3.5 Sonnet的诊断疗效.
- 调查语言 (英语,阿拉伯语,乌克兰语) 对这些LLM的诊断准确性的影响.
- 在多语言医学诊断中确定影响LLM绩效的因素.
主要方法:
- 利用了50个英语,阿拉伯语和乌克兰语的临床病例描述.
- 评估了来自ChatGPT-4o和Claude 3.5 Sonnet. 的诊断输出.
- 由临床医生小组评估结果,使用标准标准和统计分析.
主要成果:
- 在ChatGPT-4o (62.00%) 和Claude 3.5 Sonnet (60.67%) 之间,整体诊断准确度是可比的.
- 这两种模型的英语和乌克兰语表现明显好于阿拉伯语 (p<0.05).
- 近期病例描述 (2024-2025) 与较旧的病例描述 (2023) 的准确性略有下降,尽管不显著 (p>0.05).
结论:
- 在LLM诊断精度上的差异主要取决于语言,而不是特定于模型.
- 语言的复杂性,文化背景和案例类型影响了阿拉伯语LLM的表现.
- 进一步的研究是必不可少的,以提高LLM能力,在不同的语言群体中获得公平的医疗保健.
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