优化大型语言模型用于腹部和骨盆CT扫描的自动记录:背景的力量
Bryan W Buckley1, Adriano B Dias1, Yangqing Deng2
1University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, Department of Medical Imaging, University of Toronto, Toronto General Hospital, 200 Elizabeth St, Peter Munk Building, 1st Fl, Toronto, ON, Canada M5G 24C.
Radiology
|January 6, 2026
概括
像GPT-4o这样的大型语言模型 (LLM) 可以自动分配对CT扫描的最佳协议,比人类更准确. 使用详细提示的上下文工程提高了LLM的性能,而微调没有提供额外的好处.
科学领域:
- 医疗成像中的人工智能
- 放射学工作流的优化 工作流的优化
- 临床决策支持系统 临床决策支持系统
背景情况:
- 准确的成像记录对于CT扫描的诊断准确性至关重要.
- 手动编写协议是一个劳动密集型和易出错的过程.
- 大型语言模型 (LLM) 为自动化复杂的医疗任务提供了潜力.
研究的目的:
- 评估LLM在自动分配CT扫描协议中的有效性.
- 为了比较LLM表现与人类放射科医生对腹部和骨盆CT扫描.
- 评估上下文工程和微调对LLM准确性的影响.
主要方法:
- 对1448次腹部/骨盆CT扫描的回顾性分析 (2024年1月至6月).
- 使用上下文工程 (详细提示) 和微调优化了GPT-4o模型.
- 将LLM生成的协议与放射科医生定义的参考标准和人类选择的协议进行比较.
主要成果:
- 快速优化的GPT-4o实现了比人类更高的最佳协议选择率 (96.2%vs88.3%).
- 在LLM和人类之间不适当的协议率没有显著差异 (1.3%对2.4%).
- 微调GPT-4o并没有提高超出即时优化的最佳协议选择 (96.2% vs 96.2%).
结论:
- 优化的LLM (GPT-4o) 与手动方法相比,在记录腹部和骨盆CT扫描中表现出优异的性能.
- 通过详细提示进行上下文工程对于LLM协议分配是有效的.
- 除了有效的提示策略之外,进一步的微调并不能显著改善性能.
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