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加强手术前骨科沟通:对大型语言模型和临床医生生成的临床信件进行比较分析
Wilfred C Saunders1, Alexander C Glendenning2, Charles Gamble3
1Emergency Medicine, Wrexham Maelor Hospital, Wrexham, GBR.
Cureus
|February 16, 2026
概括
大型语言模型 (LLM) 通过提高字母可读性和并发症包含性来增强骨科患者的沟通. 通过LLM辅助的通信支持知情同意和共享决策.
科学领域:
- 整形外科手术 整形外科手术
- 医学沟通 医学沟通
- 医疗保健中的人工智能
背景情况:
- 有效的沟通对于骨科的知情同意至关重要.
- 医疗保健系统面临着影响患者对话的劳动力和时间限制.
- 大型语言模型 (LLM) 为简化患者与临床医生的通信提供了潜在的解决方案.
研究的目的:
- 评估在骨科实践中LLM辅助通信的可行性.
- 评估LLM生成的字母的可理解性,可读性,并包括并发症概况.
- 为了比较LLM输出与人类临床医生生成的信件.
主要方法:
- 选择了六项常见的骨科手术.
- 通过四个LLM (OpenAI o1,DeepSeek,Gemini,Copilot) 和两个人类临床医生,使用标准化提示来生成信件.
- 测量了可理解性 (PEMAT-P),可读性 (Flesch-Kincaid,炮雾,SMOG),并包括并发症.
主要成果:
- 与临床医生信件相比,LLM生成的信件表现出更好的理解性和可读性.
- 开放AI o1和双子星显示高并发症概况的合规性.
- 法学士课程的文本是以七年级和八年级的阅读水平写的,比临床医生的信件更简单.
结论:
- 在清晰度和完整性方面,LLM可以超过传统的临床医生通信.
- 整合LLM工作流可以减少行政负担并改善患者的决策.
- 对于伦理和安全的临床部署LLMs需要进一步的研究.
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