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新开发的大型语言模型在重症病例中的诊断性能:一项比较研究

Xintong Wu¹, Yu Huang¹, Qing He¹

¹Department of Intensive Care Medicine, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.

International journal of medical informatics

|August 27, 2025

概括

新开发的大型语言模型 (LLM) 对重症监护室 (ICU) 的临床决策支持有希望. 在诊断准确度方面,ChatGPT-o3领先,而开源的DeepSeek-R1也具有竞争力.

科学领域:

医学的人工智能
临床决策支持系统
危急护理医学

背景情况:

大型语言模型 (LLM) 显示出临床决策支持的潜力.
在重症监护室 (ICU) 的诊断性能还未得到充分研究.
这项研究评估了新开发的临床临床诊断方法.

研究的目的:

评估四种新型LLM在重症病例中的诊断准确性.
将这些LLM的差异诊断质量和响应质量进行比较.
确定最有效的临床决策支持的临床决策工具.

主要方法:

一项横截面比较研究评估了四种LLM:ChatGPT-4o,ChatGPT-o3,DeepSeek-V3和DeepSeek-R1.
在已发表的ICU文献中对50例严重病例进行了LLM测试.
诊断准确性,差异性诊断质量和反应质量进行了比较.

主要成果:

ChatGPT-o3获得了最高的诊断准确率 (72%),其次是DeepSeek-R1 (68%) 和ChatGPT-4o (64%). 据报道,DeepSeek-V3的准确率达到了32%.
ChatGPT-o3,DeepSeek-R1和ChatGPT-4o的表现明显超过了DeepSeek-V3的表现.
所有评估的LLM都显示出高响应质量 (完整性,清晰性,有用性),ChatGPT- o3和DeepSeek- R1显示出优异的差异诊断质量.

结论:

新开发的LLM,特别是推理模型,显示出在重症监护中支持诊断的巨大潜力.
特定领域的微调可以进一步提高LLM诊断的准确性.
开源的DeepSeek-R1的竞争性表现突出了其在资源有限的环境中的潜力.

关键词:

人工智能深度搜索不同诊断重症监护室大型语言模型

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