对多式LLM,VLM和基于代理的框架进行比较的准确性研究,用于在胸部X射线图上检测肺结节
Daria Khovanova1, Yuriy Vasilev1,2, Anton Vladzymyrskyy1,3
1Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russia.
Frontiers in digital health
|February 12, 2026
概括
这项研究评估了大型语言模型 (LLMs),用于检测胸部X射线图 (CXR) 上的肺结节. 虽然MedRAX和BiomedCLIP显示出有希望的结果,但目前的LLM准确性不足以用于临床实践.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 自然语言处理自然语言处理.
背景情况:
- 人工智能 (AI) 越来越多地融入临床环境.
- 大型语言模型 (LLM) 提供了有前途的AI助理解决方案.
- 独立的性能评估对于临床AI可行性至关重要.
研究的目的:
- 评估多式LLM在胸部X射线图 (CXR) 上检测肺结节的准确性.
- 为了比较各种专有和开源LLM用于结节检测的性能.
主要方法:
- 九个多模式的LLM被评估:拉玛3.2视觉90B,克劳德3.5索内特,克劳德3.7索内特,双子座2.0专业实验,困惑,CXR-LLaVA,XrayGPT,生物医学CLIP和MedRAX.
- 模型分析了100个CXR (50个肺结节) 的数据集.
- 计算了诊断准确度指标和ROC曲线;用于比较的是McNemar的测试.
主要成果:
- MedRAX框架和BiomedCLIP实现了最高的准确性 (0.711).
- 克劳德3.7索内特在专有单模LLM中表现最好 (准确率为0.651).
- 拉玛 3.2 视觉 90B,克劳德 3.5 索内特和双子座 2.0 专业实验显示了类似的准确性 (0.602).
结论:
- 在肺结节检测方面,MedRAX和BiomedCLIP表现出卓越的性能.
- 专有和开源模型之间没有发现显著差异,这表明开源LLM改进的潜力.
- 目前的LLM准确性不足以用于临床实施,由于数据集的局限性,结果是探索性的.
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