对ChatGPT-4,Gemini,Claude和Copilot在基于NANDA-I分类II的护理诊断中进行评估:一个比较的跨部门研究
Metin Tuncer1, Turgay Yalcinkaya2
1Faculty of Health Sciences, Department of Nursing, Gümüşhane University, Gümüşhane, Türkiye.
International nursing review
|November 28, 2025
概括
大型语言模型显示了护理诊断生成的潜力,但由于目前的局限性,需要专家验证. 建议进一步开发专门的AI工具,以确保安全的临床和教育整合.
科学领域:
- 护理中的人工智能
- 临床决策支持系统 临床决策支持系统
- 护理信息学 护理信息学
背景情况:
- 大型语言模型 (LLM) 正在成为护理实践中的潜在工具,为诊断和教育提供帮助.
- 在临床和教育护理环境中,LLM的准确性和实际应用尚未得到充分理解.
研究的目的:
- 评估LLM能够产生与NANDA-I分类法II一致的护理诊断的能力.
- 评估四个不同的LLM在各种护理领域的表现和整体准确性.
主要方法:
- 一项横截面的比较研究利用了基于NANDA-I分类II的10个现实的患者场景,涵盖了12个领域.
- 评估了四个LLM,以评估它们从患者场景中生成护理诊断的能力.
- 五位护理专家对LLM生成的诊断进行了单盲评估,以确定准确性和NANDA-I对齐.
主要成果:
- 所有评估的LLM在各个领域和整体上表现相似.
- 克劳德在测试模型中获得了最高的整体性能得分.
- 专家评估显示,评价者之间的可靠性中等,这表明专家判断的可变性.
结论:
- LLM 显示出潜力,但尚未可靠用于护理的独立临床或教育用途.
- 在临床实施之前,对LLM产生的护理诊断进行专家审查和验证是必不可少的.
- 建议开发针对护理的专门LLM,并制定强有力的监管政策和安全的AI集成培训.
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