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大型语言模型的可行性评估,用于全膝关节关节整形术的麻醉特异性术后护理说明

Dhruv Nagesh¹, Donald P Keating¹, Raghu V Divakaruni¹

¹Department of Primary Care, Kansas City University College of Medicine, Kansas City, MO, USA.

|December 17, 2025

概括

大型语言模型 (LLM) 可以为全膝关节整形手术创建准确的术后护理指令. 克劳德和GPT-4o显示出高精度,表明AI在麻醉教育中的潜力.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
麻醉学教育麻醉学教育

背景情况:

大型语言模型 (LLM) 在医学中越来越多地使用.
它们在术前教育中的应用在很大程度上仍未被探索.
有效的术后护理指令对于患者的康复至关重要.

研究的目的:

评估使用LLM来生成手术后护理指令的可行性.
为了比较四个领先的LLMs在这个任务中的表现.
评估人工智能生成的指令的准确性,清晰性,相关性,一致性和可读性.

主要方法:

四个LLM (OpenAI GPT-4o,Claude 3.7 Sonnet,DeepSeek R1,Gemini 2.0 Flash) 产生了全膝关节整形术 (TKA) 手术后护理的指令.
输出基于准确性,清晰性,相关性,一致性和可读性得分.
准确性与已建立的指南 (ERAS,ASA) 和UpToDate.com进行了基准测试.
使用弗莱什-金凯德指数评估可读性.

主要成果:

克劳德,GPT-4o和DeepSeek R1显示出比双子座更高的准确性.
克劳德和GPT-4o实现了与参考标准的完全协调.
清晰度得分在模型中相似,观察到高相关性和一致性.
可读性各不相同,Gemini产生更难以访问的文本,GPT-4o/DeepSeek R1提供更易读的内容.

结论:

LLM可以生成准确,相关和一致的术后指令.
这支持它们在麻醉教育和患者沟通方面的潜在实用性.
优化提示的可读性和普通语言可以进一步提高临床应用.

关键词:

麻醉麻醉是一种麻醉.关节质整术是指关节质整术.创新创新创新创新创新创新创新指示指示指示指示指示大型语言模型.患者教育技术技术手术后的情况.

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