在紧急骨科创伤中用于骨折检测的多模式大语言模型:诊断准确性研究
Sadık Emre Erginoğlu1, Nuri Koray Ülgen1, Nihat Yiğit1
1Department of Orthopedics and Traumatology, Ankara Sincan Training and Research Hospital, Sincan, Ankara 06935, Türkiye.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
一个多式大型语言模型 (LLM) 在检测紧急放射图片中的骨折方面显示出中等至良好的一致性,但在非位移骨折方面遇到了困难. 专家审查对于骨科创伤诊断中的患者安全至关重要.
科学领域:
- 人工智能在医学中的应用
- 放射性解读是一种放射性解读技术.
- 骨科创伤诊断 骨科创伤诊断 骨科创伤诊断
背景情况:
- 由于患者数量和时间压力很大,在急诊室 (EDs) 中精确的骨折检测至关重要,这可能会增加诊断错误.
- 具有图像识别的多式大型语言模型 (LLM) 提供了临床决策支持的潜力,但它们在ED骨科成像工作流程中的实用性尚未确定.
研究的目的:
- 评估与骨科专家相比,多式联络LLM的诊断准确性和一致性,用于ED患者的骨折检测.
- 评估LLMs在骨科创伤的现实世界临床工作流程中的表现.
主要方法:
- 对1136名连续ED患者的回顾性研究,这些患者被转介给了骨科.
- 紧急放射和临床数据由多模式LLM (2025版本) 处理.
- 通过使用科恩的卡帕,灵敏度,特异性和准确性,LLM输出与盲目的骨科专家的最终诊断进行了比较.
主要成果:
- 该LLM与专家达成了中等到良好的诊断协议 (71.1%; κ = 0.634).
- 灵敏度为76.9%,特异性为66.8%;对于膝盖创伤,一致性最高.
- 虚假阴性病例 (16.2%) 常见于非移位骨折,特别是在脚和脚部区域 (分别为30.4%和17.4%的FN率).
结论:
- 多模式LLM在ED骨科成像中显示出作为决策支持工具的希望,但尚未成为可靠的独立诊断系统.
- 在检测微妙的,非位移骨折的LLM的局限性构成患者安全风险,强调了专家解释的持续需求.
- 在例行临床采用之前,需要进一步的前性,多中心研究,使用优化的算法.
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