RadOncRAG:一种新的检索增强生成框架改善了辐射瘤学中大型语言模型基准性能
Nikhil Gautam Thaker1,2, Navid Redjal1, Adam Dicker3
1Capital Health, Pennington, NJ.
JCO clinical cancer informatics
|November 14, 2025
概括
检索增强生成 (RAG) 在辐射瘤学中改进了非推理的大型语言模型 (LLM),但不是推理模型. 通过以证据为基础的解释,RAG提供了一种具有成本效益的方法,以增强临床决策支持.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 医学教育 医学教育
背景情况:
- 大型语言模型 (LLM) 在瘤学中显示出潜力,但面临诸如幻觉和过时数据等挑战.
- 检索增强生成 (RAG) 通过整合外部的,特定领域的知识来解决这些局限性.
研究的目的:
- 评估RAG管道对辐射瘤学中各种LLM的性能的影响.
- 使用放射瘤学检查数据集,将零射击LLM性能与RAG增强性能进行比较.
主要方法:
- 15名LLM从2021年美国放射学学院的培训考试中对298个问题进行了测试.
- 实施了一个RAG管道 (Iridium模型),查询一个专门的辐射瘤数据库以增加提示.
- 在零射击和RAG增强工作流程之间进行了性能比较.
主要成果:
- 较大的LLM表现出更高的零射击精度,其中一些比毕业生居民表现更好.
- 像GPT-4o这样的推理模型在没有RAG的情况下实现了高精度;RAG没有提高他们的性能.
- RAG提高了非推理模型的性能,在临床,生物学和物理知识领域获得了特定领域的收益.
- 多数投票提高了总的准确性,而RAG和推理模型增加了计算成本.
结论:
- 一个特定于放射性瘤学的RAG管道通过结合特定于领域的证据来提高非推理的LLM性能.
- 在这种情况下,RAG并不能提高高级推理模型的性能.
- 通过提供可引用的解释,RAG为临床决策支持的广泛模型培训提供了一种高效,具有成本效益的替代方案.
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