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术后并发症管理:大型语言模型如何与人类的专业知识相匹配?

Sophie-Caroline Schwarzkopf^1,2, Jean-Paul Bereuter¹, Mark Enrik Geissler¹

¹Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

PLOS digital health

|August 1, 2025

概括

大型语言模型 (LLM) 在管理手术患者护理方面表现有前途. GPT-4在识别术后并发症方面表现出很高的准确性,帮助医疗团队进行患者分拣和管理策略.

科学领域:

医疗信息学医疗信息学
在外科手术中使用人工智能
手术患者护理手术患者护理

背景情况:

术后并发症的有效管理对于手术结果至关重要.
大型语言模型 (LLM) 为医疗专业人员提供了潜在的支持.
评估手术患者护理的LLM能力是一个新兴的研究领域.

研究的目的:

为了比较三个最先进的LLM与人类外科护理人员在治疗术后并发症方面的表现.
评估LLM在手术后患者的分组,诊断和管理计划中的准确性.

主要方法:

六个现实的术后病例被用于评估.
询问了GPT-3,GPT-4和Gemini-Advanced,并将他们的答案与人类专家的评估进行了比较.
绩效的评估是基于医疗正确性,连贯性和分拣,诊断和管理计划的完整性.

主要成果:

在正确识别术后并发症方面,GPT-4显著优于人类 (96.7%对比76.3%).
GPT-3和GPT-4提供了全面的诊断和治疗管理计划.
双子座-高级显示局限性,经常审查输出和提供更少的建议.

结论:

毕业生,特别是GPT-4,在解释术后护理场景方面表现出相当大的能力.
通过提供准确的管理建议,LLM显示出增加手术常规护理的潜力.
进一步开发LLM可以提高它们在支持外科手术团队中的实用性.

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