针对肺胸部检测的多式联运大型语言模型的大规模评估
Hamza Eren Güzel1, Cemre Özenbaş2, Ali Murat Koç3
1University of Health Sciences Türkiye, İzmir City Hospital, Clinic of Radiology, İzmir, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|February 12, 2026
概括
多式人工智能模型显示高特异性但低灵敏度检测肺胸在胸部X射线. 这些人工智能工具尚未可靠地排除肺胸炎,但可能有助于放射科医生在临床工作流程中.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 胸部X射线 (CXR) 上的肺胸部检测在急症护理中至关重要.
- 先进的AI模型为图像解释提供了潜力,但它们的诊断可靠性需要评估.
- 本研究评估了用于肺胸部检测的最先进的多式模式.
研究的目的:
- 评估三种领先的多式人工智能系统 (GPT-4o,Gemini 2 Pro,Claude 4 Sonnet) 在检测肺胸部的诊断性能.
- 通过使用大型注释CXR数据集,分析不同肺胸大小的性能.
主要方法:
- 来自SIIM-ACR肺胸部细分数据集的10675个CXR的分析.
- 使用统一的,基于图像的方法对三种多式联运模式进行评估.
- 将模型输出与准确度,灵敏度,特异性,精度和F1分数的参考结果进行比较,并根据肺胸大小对子组进行分析.
主要成果:
- 肺胸病的患病率为22.3%.
- 所有模型都实现了高特异性 (>0.90),但低灵敏度 (0.16-0.36).
- 双子座2显示出最佳的整体精度 (0.79) 和特异性 (0.95); 克劳德4显示出更高的灵敏度. 随着肺胸大小的增加,性能有所改善,但小病变仍然具有挑战性. 在模型之间发现了统计学上显著的差异 (P < 0.050).
结论:
- 目前的多式大型语言模型 (LLM) 在识别正常的CXR方面表现出强大的可靠性,但在检测小或微妙的肺胸部方面能力有限.
- 低灵敏度限制了它们作为独立工具的使用,以排除肺胸炎.
- 未来的改进可能使这些人工智能模型能够通过提高工作流效率和诊断信心来支持放射科医生.
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