大型语言模型的多中心基准测试,用于肺癌查中的临床决策支持
Zhiyun Duan1, Xu Huang2, Rong Lu3
1Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China; Intelligent Medicine Institute, Fudan University, Shanghai, China.
Cell reports. Medicine
|November 22, 2025
概括
大型语言模型 (LLM) 对肺癌查建议有希望. GPT-4和Claude 3 Opus提供了高精度和可读性,展示了临床决策支持的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 放射学和医学成像学 医学成像学
背景情况:
- 大型语言模型 (LLM) 正在评估临床使用,但它们在生成肺癌查管理建议方面的有效性尚未得到充分确立.
- 肺癌查指南需要准确,易于阅读的建议才能有效实施.
研究的目的:
- 评估领先的大型语言模型的可读性,准确性和一致性,以生成肺癌查管理建议.
- 评估不同医疗机构LLM的表现.
主要方法:
- 一项横截面的多中心研究分析了148份匿名低剂量计算机断层扫描 (CT) 报告.
- 四个LLM (GPT-3.5,GPT-4,克劳德3小诗,克劳德3大作) 在推质量方面进行了评估.
- 绩效被评估为可读性,临床准确性和机构间的一致性.
主要成果:
- 克劳德3 Opus产生了最易于阅读的建议; GPT-4实现了最高的临床准确性.
- 模型的性能在三个医疗机构中是一致的,表明了稳健性.
- 像GPT-4o和DeepSeek-R1这样的新型号显示了与GPT-4相比的性能.
结论:
- 大型语言模型,特别是GPT-4和Claude 3 Opus,显示出在肺癌查中增强临床决策支持的巨大潜力.
- 在各种医疗保健环境中LLM的稳定性表明它们在提高查建议质量的广泛适用性.
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