在生成性人工智能时代的护理判断:关于急诊护士临床决策表现的跨国研究
C Levin1, A Zaboli2, G Turcato3
1Faculty of School of Life and Health Sciences, Nursing Department, The Jerusalem College of Technology-Lev Academic Center, Jerusalem, Israel.
International journal of nursing studies
|September 21, 2025
概括
生成型人工智能模型在临床决策中显示出高准确性,但缺乏急诊护士的上下文敏感性. 人类临床判断对于复杂的患者护理至关重要.
科学领域:
- 紧急护理 紧急护理
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 临床决策在急诊护理中至关重要.
- 生成型人工智能 (AI) 越来越多地融入医疗保健.
- 在复杂的,情境敏感的临床场景中,生成人工智能的有效性需要评估.
研究的目的:
- 将急诊护士的临床决策与生成AI模型进行比较.
- 评估AI和护士在严重性评估,住院决定和测试选择中的表现.
- 探索影响决策准确性的人口和专业因素.
主要方法:
- 与82名急诊护士 (意大利和以色列) 进行的前性观察研究.
- 护士和生成AI模型 (Claude-3.5,ChatGPT-4.0,Gemini-1.5) 独立评估了5个标准化的临床病例.
- 统计分析包括ANOVA,千平方,后勤回归和ROC分析.
主要成果:
- 生成型人工智能模型实现了更高的整体决策准确性和专家对齐.
- 在住院决定和严重程度评估中发现的差异.
- 护士表现出更大的变化,表明他们依赖临床直觉和上下文推理.
结论:
- 由于一致性,生成人工智能显示出作为临床决策支持工具的潜力.
- 人工智能模型缺乏紧急护理所必需的上下文敏感性.
- 在紧急护理中,人类临床判断仍然是不可或缺的.
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