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通过提取增强的LLM代理来进行脊柱手术的可解释和与证据相关的建议

Yiren Li¹, Mingyu Lv¹, Liang Cheng¹

¹Department of Spine Surgery, Center for Orthopedic Surgery, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.

European journal of radiology

|February 21, 2026

概括

双子座2.5 Flash和DeepSeek在使用新型RAG框架的脊椎疾病决策支持方面表现出卓越的表现. 这项研究突出了域调整的大型语言模型 (LLM) 提高临床护理的潜力.

科学领域:

人工智能在医学中的应用
脊柱外科手术决策支持
临床信息学临床信息学

背景情况:

脊柱疾病的临床决策需要整合成像,神经状态,骨完整性和患者目标.
大型语言模型 (LLM) 提供了智能决策支持的潜力,但需要严格评估可靠性和可解释性.

研究的目的:

提出和评估一种基于增强生成 (RAG) 的新型检索框架,用于脊髓疾病的决策支持.
在这个框架内,系统地比较四种先进的LLM代理 (Gemini 2.5 Flash,DeepSeek,GPT-4o,GPT-4o-mini) 的性能.

主要方法:

开发了一个RAG框架,包括特定领域的提示,结构化响应和证据跟踪.
用200个真实的脊柱病例来评估诊断,治疗和后续任务.
五位脊椎外科医生使用11维标签评估了LLM输出,并对组间差异进行了统计分析.

主要成果:

双子 2.5 闪光实现了最高的整体得分,明显优于其他型号.
在差异诊断的完整性方面,DeepSeek表现出卓越的性能.
在脊柱特定的临床推理中观察到实质性的跨模型变异性.

结论:

双子 2.5 Flash 和 DeepSeek 显示出卓越的临床准确性,全面性和用于脊柱决策支持的可用性.
适应领域的RAG剂显示出增强基于证据的脊柱护理的前景.
未来的工作应该探索多式联运数据集成和潜在的临床验证.

关键词:

临床决策支持临床决策支持专家评估框架专家评估框架大型语言模型 (LLM)检索增强生成 (RAG) 是指检索增强生成.脊髓疾病脊髓疾病

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