优化麻醉护理:利用大型语言模型进行有效的手术前规划
Mary Scott-Herring1, Katherine Thorpe, Nancy Crowell
1Doctor of Nurse Anesthesia Practice (DNAP) Program (Drs Scott-Herring, Thorpe, and McAuliffe) and School of Nursing (Dr Crowell), Georgetown University, Washington, DC.
Computers, informatics, nursing : CIN
|December 24, 2025
概括
这项研究发现,ChatGPT可以根据患者病史创建适当的麻醉护理计划. 然而,它需要对关键细节进行特定的输入,例如呼吸机设置和药物剂量.
科学领域:
- 麻醉学 麻醉学
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 人工智能 (AI) 的快速发展需要对其临床应用进行研究.
- 在临床实践中,特别是麻醉中,了解大语言模型 (LLM) 的实用性存在差距.
- 对医疗保健应用程序的AI工具的评估对于安全有效的整合至关重要.
研究的目的:
- 评估大型语言模型ChatGPT在生成麻醉护理计划中的有效性.
- 确定使用人工智能来起草关键患者护理文件的优点和局限性.
主要方法:
- 一年级护士麻醉学住院医生将关键麻醉护理计划变量输入到ChatGPT.
- 人工智能产生的麻醉护理计划被三个专家教师独立审查.
- 分析重点关注ChatGPT对基本麻醉变量输出的准确性和完整性.
主要成果:
- 根据提供的病史和家庭药物,ChatGPT成功生成了适当的麻醉计划.
- 人工智能模型在充分解决关键变量方面表现出局限性,例如呼吸机设置,液体管理和药物剂量等.
- 聊天GPT的输出质量取决于用户提供的输入的清晰度和具体性.
结论:
- 像ChatGPT这样的大型语言模型显示出在创建麻醉护理计划中的辅助工具的潜力.
- 在临床环境中有效利用人工智能需要有知识的医疗保健专业人员提供明确,详细的投入.
- 需要进一步的研究来完善人工智能的能力,以制定全面的麻醉护理计划.
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