在患有早期非小细胞肺癌的患者中,使用最佳证据主题报告对大型语言模型进行基准测试
Vivek Chaudhuri1, Alessandro Brunelli2,3, Peter Tcherveniakov2,3
1Lady Margaret Hall, University of Oxford, Oxford, United Kingdom.
Interdisciplinary cardiovascular and thoracic surgery
|February 9, 2026
概括
大型语言模型 (LLM) 显示出潜力,但尚未可靠地回答临床问题. 最好的证据主题 (BET) 方法仍然是基于证据的临床决策的黄金标准.
科学领域:
- 人工智能在医学中的应用
- 临床证据综合研究
背景情况:
- 大型语言模型 (LLM) 提供对话性AI功能.
- 评估临床查询的LLM准确性至关重要.
研究的目的:
- 评估LLM在回答患者特定临床问题的表现.
- 将LLM的成果与最佳证据主题 (BET) 方法进行基准测试.
主要方法:
- 问询了LLM关于机器人辅助胸部手术 (RATS) 与视频辅助胸腔镜手术 (VATS) 叶切除术的比较结果.
- 采用结构化的BET协议来识别和综合相同临床问题的最佳可用证据.
- 对BET的证据检索涉及广泛的数据库搜索,产生了12篇关键论文.
主要成果:
- LLM的回复很快,但往往缺乏相关性,含有不准确性 (幻觉),并面临访问障碍 (版权,付费墙).
- 在人类专业知识的指导下,BET提供了更可靠,更细微和更为定制的信息.
- 对于T1cN0M0NSCLC的RATS和VATS叶切除术之间没有发现显著的结果差异,除了RATS患者的住院时间较短.
结论:
- 目前的LLM在临床问题回答方面并不完全可靠.
- 一个标准化的LLM-BET协议可以促进在各种临床场景中对LLM输出进行比较.
- 此协议还可用于分析和比较现有和未来的不同LLM模型.
关键词:
非小细胞肺癌 (NSCLC) 是一种非小细胞肺癌.RATS机器人辅助胸腔镜手术VATS视频辅助胸腔镜手术聊天GPT 聊天 在GPT 聊天双子座是一个双子座.格洛克·格洛克 (Grok Grok) 是一个古老的语言.微软的复制飞行员更多相关视频
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