多模式大语言模型与急诊医生对烧伤评估:一个前性的非劣势研究
Ahmet Aykut1, Ali Rıza Karayıl2, Cem Yıldırım3
1Department of Emergency Medicine, SBU Van Education and Research Hospital, Van, Türkiye. ahmet.aykut@gmail.com.
Scandinavian journal of trauma, resuscitation and emergency medicine
|February 6, 2026
概括
一个新的AI模型显示,在估计烧伤大小 (身体总表面积) 方面,其性能与医生相比不差,但在准确的烧伤深度分类方面却存在困难. 这表明人工智能可以协助TBSA估计,但临床判断对于深度评估至关重要.
科学领域:
- 人工智能在医学中的应用
- 紧急医疗 诊断 诊断 诊断 诊断
- 医学成像分析 医学成像分析
背景情况:
- 准确的烧伤评估对于患者管理至关重要,但患有临床间的变化.
- 大型语言模型在紧急情况下提供了可扩展,基于图像的决策支持的潜力.
- 人工智能模型与临床医生和专家标准的前景验证是有限的.
研究的目的:
- 为了评估诊断准确性和一致性的多式联络大型语言模型烧伤大小和深度评估.
- 使用强大的参考标准,将AI性能与急诊医生进行比较.
- 为了确定AI在总体表面积 (TBSA) 估计方面的非劣势.
主要方法:
- 在第三级急诊室进行前性诊断准确性研究.
- 急性烧伤区域的标准化三视图照片由多式联络LLM和18名急诊医生分析.
- 与专家小组对TBSA估计和燃烧深度分类的参考标准进行比较.
主要成果:
- 人工智能模型在区域一级的TBSA估计上表现出对医生的非劣势 (平均绝对误差为1.40比0.89个百分点).
- 与AI模型 (kappa 0.14) 相比,医生共识显示烧伤深度分类 (kappa 0.65) 的一致性要大得多.
- 人工智能模型系统地低估了更深的烧伤.
结论:
- 多模式人工智能可以在紧急烧伤护理中作为TBSA估计的辅助.
- 目前,人工智能对烧伤深度分类的性能不足,需要临床医生主导的评估.
- 对AI在燃烧深度评估中进行进一步的开发和外部验证是必要的.
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