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评估用于成像模式选择的大型语言模型:有可能减少不必要的对比剂使用和辐射暴露

Eren Çamur¹, Turay Cesur², Yasin Celal Güneş³

¹Department of Radiology, Ministry of Health Ankara 29 Mayis State Hospital, Ankara, Türkiye.

Clinical imaging

|August 2, 2025

概括

大型语言模型 (LLM) 在选择合适的成像方式方面表现出很高的准确性,表现优于临床医生和匹配的初级放射科医生. 这些人工智能工具显示出在放射学中支持临床决策的巨大潜力.

科学领域:

人工智能的人工智能
放射学放射学是一门学科.
医疗决策支持医疗决策支持

背景情况:

大型语言模型 (LLM) 提供了人工智能的变革潜力,特别是在革命性的放射性决策方面.
这项研究独特地评估了LLM在选择适当的成像方法方面的表现,与临床专家相比.

研究的目的:

评估各种LLM在不同临床场景中推最佳成像方式方面的表现.
将LLM建议与不同经验水平的临床医生和放射科医生的建议进行比较.

主要方法:

一个横截面的实验设计评估了240个临床情景 (ACR和多方面的),使用三个不同的提示.
四名LLM与四名临床医生和四名放射科医生进行了评估,评估了模式选择,对比剂使用和X射线包含.
通过短期和长期评估来评估可重复性.

主要成果:

所有的LLM都在提示符中的模式建议中达成了完全一致 (κ=1).
在ACR场景中,DeepSeek-R1表现出卓越的准确性 (98.3%),优于其他LLM.
在现实的场景中,DeepSeek-R1与初级放射科医生的性能相匹配,并超过临床医生和住院医生的性能.

结论:

在各种临床环境中,LLM在选择适当的成像方式方面表现出了显著的潜力.
法律学可以作为有价值的支持工具,增强放射学中的临床实践.
这些发现凸显了人工智能在医学成像决策支持中的日益重要的作用.

关键词:

适当性适当性聊天GPT 聊天GPT 聊天采用成像方式的方式.大型语言模型建议建议是一个建议.选择选择选择选择

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