十个大型语言模型和常规搜索引擎对麻醉学临床决策支持的比较:专家协议和医生的看法
Oliver Cafferty1,2, Sean D Jeffries1,2, Eric D Pelletier1,2
1From the Department of Surgical Interventional Sciences, McGill University, Montreal, Canada.
Anesthesia and analgesia
|January 29, 2026
概括
对于麻醉学问题,DeepSeek R1和Claude-Sonnet 3.5在大型语言模型 (LLM) 中表现优异. 在临床决策支持中广泛采用人工智能之前,需要进一步验证.
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
- 麻醉学研究 麻醉学研究
背景情况:
- 大型语言模型 (LLM) 显示出产生医疗信息的潜力.
- 它们在麻醉学临床决策支持中的实用性在很大程度上未经检查.
- 这项研究评估了LLM准确性和临床相关性,用于麻醉查询.
研究的目的:
- 评估各种LLM对于麻醉学问题的准确性和临床相关性.
- 将LLM的表现与传统的在线搜索方法进行比较.
- 评估麻醉师在临床实践中对AI的看法.
主要方法:
- 十个LLM和两个谷歌搜索条件用十个麻醉问题进行了测试.
- 响应被四位经过董事会认证的麻醉师在10点的利克特级别上独立评价.
- 一个医生感知问卷捕获了AI的使用,信任和依赖传统来源.
主要成果:
- 在LLM的表现上有显著差异,DeepSeek R1得分最高 (7.7),Gemini 2.0 Flash得分最低 (5.2).
- 深度搜索R1显著超过了其他几个LLM (P <.001).
- 临床医生报告AI使用有限,优先考虑来源可信性和传统资源.
结论:
- 深度搜索R1和克劳德-索内特3.5提供了最符合麻醉学专家判断的反应.
- 可变的LLM表现和临床医生的怀疑强调了严格验证的必要性.
- 在将AI整合到常规麻醉学决策支持中之前,进一步的研究是必不可少的.
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