人工智能临床推理在板式临床图片中:一项比较研究
Lorela Gjunkshi1, Ledio Gjunkshi2, Kenneth A Quezada2
1College of Medicine, Saba University School of Medicine, The Bottom, NLD.
Cureus
|November 17, 2025
概括
四个人工智能 (AI) 大语言模型 (LLM) 用医疗执照考试问题测试了诊断准确性. 克劳德·索内特4表现最好,显示了AI.
科学领域:
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地被探索在医疗保健应用中的潜力.
- 评估人工智能平台的诊断准确性对于了解它们在医学教育和实践中的实用性至关重要.
- 美国医学执照考试 (USMLE) 第1步的临床图片为诊断推理提供了标准化的评估.
研究的目的:
- 评估四个领先的大型语言模型 (LLM) 人工智能 (AI) 平台的诊断准确性.
- 评估LLM在从临床细节中产生初级和差异诊断方面的能力.
- 在模拟诊断推理任务中比较不同LLM平台的性能.
主要方法:
- 使用了10个USMLE Step 1临床标题题,删除了答案选择,以创建开放式的诊断挑战.
- 在四个LLM平台 (ChatGPT GPT-4o-mini,Meta AI Llama 4,Google Gemini 2.0 Flash,Claude Sonnet 4) 被提示进行初级和差异诊断.
- 答案使用评估诊断准确性的标题得分,每个模型的最高得分为20分.
主要成果:
- 克劳德·索内特4实现了完美的准确性 (20/20,100%),其次是谷歌双子座 (19/20,95%),聊天GPT GPT-4o-mini (17/20,85%) 和Meta AI Llama 4 (13/20,65%).
- 所有测试的LLM都显示出临床相关的诊断推理.
- 在不同的人工智能平台上观察到诊断优先级和准确度的显著变化.
结论:
- 目前的LLM显示出作为增强诊断推理和医疗培训的补充工具的巨大潜力.
- 从复杂的病例中产生准确诊断的LLM的能力支持他们在临床决策支持和教育中的价值.
- 在临床整合之前,性能变化需要仔细实施,解决诸如偏见和隐私之类的伦理问题.
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