放射学协议的自动预测使用检索增强代增强代.
Conrad Testagrose1, Panagiotis Korfiatis2, Justin Benfield2
1Center for Augmented Intelligence in Imaging, Mayo Clinic, Jacksonville, FL, 32224, USA. Testagrose.Conrad@mayo.edu.
Journal of imaging informatics in medicine
|March 17, 2026
概括
大型语言模型 (LLM) 显示了自动化放射性协议选择的前景. 检索增强生成 (RAG) 在某些站点提高了准确性,但需要特定站点的调以获得最佳性能.
科学领域:
- 放射学中的人工智能
- 临床决策支持系统 临床决策支持系统
- 医疗信息学 医疗信息学
背景情况:
- 放射学协议的选择是复杂的,耗时的,容易出现错误.
- 自动化方法面临着诸如阶级不平衡和特定地点的变化等挑战.
- 大型语言模型 (LLM) 提供了提高协议选择效率的潜力.
研究的目的:
- 评估LLM在大规模放射性协议选择中的有效性.
- 为了比较检索增强生成 (RAG) 与直接微调的协议选择.
- 评估RAG对不同临床场所的准确性,弃权率和适应能力的影响.
主要方法:
- 经过培训的特定站点的Llama 3.2 3B LLMs使用三个梅奥诊所站点的患者报告.
- 通过整合分区范围的FAISS索引来实现RAG的上下文证据.
- 微调非RAG和RAG增强模型的性能比较,使用宏和加权F1分数.
主要成果:
- 无论是RAG还是非RAG模型,在各个地点都表现出强的基线性能.
- 在亚利桑那州和佛罗里达州,RAG显著改善了宏观F1,但在罗切斯特没有.
- 在大多数地点,RAG引入了一个可解释的弃权机制,基线率较低 (1-2.5%).
结论:
- LLM,特别是RAG,可以支持可靠的放射性协议大规模选择.
- 在不同站点上,RAG的有效性是异质的,需要特定站点的调整.
- RAG的可适应的检索索引和弃权机制为临床工作流集成提供了运营优势.
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