评估大型语言模型生成的大脑MRI协议:GPT4o,o3-mini,DeepSeek-R1和Qwen2.5-72B的性能
Su Hwan Kim1,2, Severin Schramm3, Lena Schmitzer3
1Institute of Diagnostic and Interventional Radiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany. suhwan.kim@tum.de.
European radiology
|September 3, 2025
概括
大型语言模型 (LLM) 在自动化大脑MRI协议方面具有前景. 最好的表现是O3迷你模型,
科学领域:
- 医学成像
- 放射学中的人工智能
- 神经辐射学
背景情况:
- 脑部MRI记录是一个耗时的,非解释性的任务,
- 自动化协议生成可能会减轻这种负担.
研究的目的:
- 评估各种大型语言模型 (LLM) 产生序列级大脑MRI协议的能力.
- 将LLM生成的协议与放射学居民创建的协议的性能进行比较.
主要方法:
- 对150例脑部MRI病例进行了回顾性分析.
- 使用GPT-4o,o3-mini,DeepSeek-R1和Qwen2.5-72B来生成协议,包括在环境中学习本地标准.
- 使用精度指数 (冗余和缺失序列的总和) 来评估协议质量.
主要成果:
- 在增强条件下,o3-mini表现最好,精度指数为1.94±1.25.
- 所有的LLM都表现出在上下文学习 (adj. p < 0.001) 的情况.
- 最高效的LLM (o3-迷你增强) 的精度指数与放射科住院医生的精度指数相当 (1.77 ± 1.28).
结论:
- 在半自动化脑磁共振记录方面,LLM具有显著的潜力.
- 在上下文学习大大提高了LLM协议生成的准确性.
- 在这种应用中,o3-mini和GPT-4o特别有前途.
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