大语言模型在复杂麻醉决策中的表现:高风险患者四个LLM的比较研究
Qian Ruan1, Jinghong Shi1, Yunke Dai1
1Department of Anesthesiology, Clinical Medical College, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
Journal of medical systems
|October 1, 2025
概括
与其他大型语言模型 (LLM) 相比,DeepSeek-R1在重症产科和老年患者的麻醉决策方面表现出卓越的表现. 这项研究突出了LLMs的重点.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 麻醉学 麻醉学
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于临床决策支持.
- 在麻醉等复杂的医疗场景中评估LLM的表现至关重要.
- 临界护理环境,特别是对于产科和老年患者来说,对决策提出了独特的挑战.
研究的目的:
- 为了比较四个领先的LLM的麻醉决策性能:ChatGPT-4o,克劳德3.5索内特,DeepSeek-R1和Grok 3.
- 分析这些LLM在重症患者的不同外科专业的可靠性.
- 为了评估特定患者群体的LLM绩效:产科和老年医生.
主要方法:
- 一个前性比较分析,涉及标准化案例评估.
- 在8个专业中评估了30个复杂的手术病例 (10个产科,20个老年病例).
- 一个12维框架和5名麻醉师的6维评估被用来评分LLM的表现.
主要成果:
- DeepSeek-R1获得了最高的整体绩效评分 (51.43 ± 2.74),明显超过其他LLM (P < 0.001).
- 在产科 (52.00 ± 1.83) 和老年病例 (51.15 ± 3.10) 的评估中,DeepSeek-R1表现出色.
- 所有模型在肝胆,烧伤和胸部手术方面表现最好,而DeepSeek在决策逻辑和应急计划方面表现强.
结论:
- 在复杂的患者群体中,DeepSeek-R1在麻醉决策方面表现出评估的LLM中最强大的性能.
- 在不同的外科专业中,LLM的成绩可能会有所不同,因此需要进一步调查.
- 在麻醉中临床整合LLM应考虑到专业特定的细微差别和决策过程.
关键词:
麻醉决策的决策过程聊天GPT 聊天 在GPT 聊天克劳德·克劳德·克劳德 (Claude Claude) 是一个在DeepSeek搜索中深入搜索.格洛克·格洛克 (Grok Grok) 是一个古老的语言.大型语言模型.更多相关视频
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