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Updated: Jan 23, 2026

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放射学数值任务中的大语言模型:彻底评估和错误分析.

Ali Nowroozi1, Masha Bondarenko1, Adrian Serapio1

  • 1Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, CA, USA.

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概括

大型语言模型 (LLM) 在放射学数值任务中进行了评估. 强化学习 (RL) 模型表现出一致的高性能和准确性,没有发现数学错误.

关键词:
数据提取数据提取大型语言模型.数学 数学 是一个数学.数字数字 数字数字放射学报告 放射学报告 放射学报告推理 推理是一种推理.

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 在医疗保健中的自然语言处理.

背景情况:

  • 大型语言模型 (LLM) 在处理临床文本方面表现有前途.
  • 在放射学等特定医疗领域评估LLM绩效至关重要.

研究的目的:

  • 评估各种LLM在放射学数字提取和判断任务上的表现.
  • 在这些任务中对LLM输出进行详细的错误分析.

主要方法:

  • 定义了六个放射学任务:三个提取 (T分数,CBD直径,肺结节大小) 和三个判断 (PET超代谢,骨质疏松症,CBD扩张).
  • 评估的LLM包括Llama 3.1 8b,DeepSeek R1蒸的Llama 8b,OpenAI o1-mini和OpenAI GPT-5-mini,使用的是MIMIC III和机构数据库的数据.
  • 对所有不正确的LLM输出进行了手动审查和错误分析.

主要成果:

  • 对于提取任务,非RL模型 (o1-mini,GPT-5-mini) 实现了>95%的准确性,而Llama显示了变化 (86%-98.7%).
  • 在判断任务中,o1-mini和GPT-5-mini的准确率分别为91.7%和99.0%,在骨质疏松症检测中准确率为100%.
  • 在o1-mini和GPT-5-mini输出中没有发现数学错误. 只有答案的格式对Llama和DeepSeek的蒸Llama性能产生了负面影响.

结论:

  • 强化学习 (RL) 推理LLMs在放射学数值任务中表现出一致的高性能和准确性,没有数学错误.
  • 非RL模型也可以根据具体任务的复杂性实现可接受的性能.