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最新的AI模型,RAG和MCP在肺癌相关问题上的表现

Xinjie Zhao¹, Miaomiao Yang², Kang Tian¹

¹Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

|March 16, 2026

概括

大型语言模型 (LLM) 在临床环境中表现相似. 整合检索增强生成 (RAG) 或模型上下文协议 (MCP) 显著提高了医学信息的LLM准确性和清晰度.

科学领域:

人工智能在医学中的应用
自然语言处理自然语言处理.
临床决策支持临床决策支持

背景情况:

大型语言模型 (LLM) 的快速发展引发了对临床可靠性的担忧.
诸如幻觉和不充分的引用等问题限制了LLM在医疗保健中的使用.
在医疗应用中确保LLM的准确性和可靠性至关重要.

研究的目的:

评估六个领先的LLM在临床环境中的表现.
评估提取增强生成 (RAG) 和模型上下文协议 (MCP) 对LLM准确性的影响.
为了比较LLM在回答肺癌诊断和治疗问题的能力.

主要方法:

六个LLM (GPT,o3,Gemini,Grok,Qwen3,Claude) 进行了评估.
模型是根据肺癌临床指导方针从一个池中测试50个问题.
对基线LLM和用RAG或MCP增强的LLM进行了性能分析.

主要成果:

基线LLM准确性各不相同,o3 (50%),GPT (48%) 和Gemini (48%) 的表现最高.
与基线模型相比,RAG和MCP集成显著提高了LLM准确性.
改进的LLM显示词汇丰富度和语义噪音降低,清晰度和准确度提高.

结论:

目前的LLM在专业医疗问题上表现相似.
RAG和MCP的整合大大提高了LLM的准确性,提高了响应质量.
这些外部知识库技术为可靠的临床LLM应用提供了有希望的途径.

关键词:

聊天GPT 聊天在GPT 聊天人工智能的人工智能是人工智能.大型语言模型肺癌是一种肺癌.模型模型的协议上下文协议上下文提取增强生成的提取

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