弥合潜力与实践之间的差距:对护理中生成人工智能的综合性审查
Lillian Campbell1, Victoria L Tiase2
1Portola High School, Irvine, CA.
Computers, informatics, nursing : CIN
|February 9, 2026
概括
由于教育差距,道德问题和工作流程问题,护士采用生成人工智能 (GAI) 是有限的. 护士参与GAI设计和人工智能教育是成功整合的关键.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 人工智能在护理中的应用
- 临床信息学是一种临床信息学.
背景情况:
- 生成型人工智能 (GAI) 和大型语言模型 (LLM) 在医疗保健中提供了潜在的好处,包括增强决策和EHR效率.
- 尽管GAI具有潜力,但其在护理实践中的采用仍然非常有限.
- 本次审查解决了阻碍GAI融入护理工作流程的障碍.
研究的目的:
- 综合现有的关于在护理中采用生成人工智能 (GAI) 的文献.
- 识别和探索将GAI工具集成到护理实践中的障碍.
- 为克服发现的障碍提供建议.
主要方法:
- 在2022年11月至2025年7月期间发表的同行评审文献的综合性审查.
- 通过标题,摘要和全文来选文章的资格.
- 综合主题,重点关注影响护士采用GAI的伦理,教育和工作流因素.
主要成果:
- 十项研究符合纳入标准,包括定性描述性设计,病例报告和一项实验性临床试验.
- 确定的主要障碍包括教育缺陷,道德考虑,缺乏数据透明度和工作流失调整.
- 由于这些已识别的障碍,护士在使用GAI工具时表现出犹.
结论:
- 克服障碍需要护士积极参与GAI工具的设计,实施和评估.
- 有针对性的教育举措和强大的治理模式对于培养信任和可用性至关重要.
- 以护士为中心的GAI集成可以提高劳动力效率并保持患者安全.
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