护理中的机器学习:跨学科审查
Dimitrios Kosmidis1, Dimitrios Simopoulos2, Konstantinos Anastasopoulos3
1Department of Nursing, Democritus University of Thrace, Alexandroupolis, GRC.
Cureus
|August 4, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在改变医疗保健,但护士需要更多的教育来使用这些工具. 本综述探讨了AI/ML应用,挑战以及将技术整合到护理护理中的重要支柱.
科学领域:
- 护理信息学 护理信息学
- 医疗保健 人工智能 医疗保健 人工智能
- 机器学习在医学中的应用
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 的快速进步正在显著影响医疗保健服务.
- 由于有限的正规教育和培训,护士们经常在采用这些技术时面临犹.
- 本综述探讨了在护理实践中对AI/ML的全面理解的需要.
研究的目的:
- 介绍基本的AI/ML概念,评估指标和护理特定的例子.
- 在护理研究和临床实践中对算法应用进行目录.
- 描述与在护理中实施AI/ML相关的伦理和实际挑战.
主要方法:
- 在主要数据库 (PubMed,Embase,Scopus,IEEE Xplore,ACM数字图书馆) 中从2015年至2024年进行了系统的文献搜索.
- 检索了1445个记录,并分析了61篇符合纳入标准的同行评审论文.
- 包括专注于AI/ML开发,实施或评估在护理环境中的研究.
主要成果:
- 监督学习模型占主导地位,常见的评估指标包括准确性,AUROC,精度回忆和F1分数.
- 确定的主要应用包括风险评估,临床决策支持,患者监测,工作流的优化,自然语言处理和教育.
- 采用障碍包括监管/道德问题,数据质量,模型透明度和护理专用ML培训不足.
结论:
- 在护理中成功地利用ML需要跨学科的合作,整合到护理教育中,以及将以人为中心的护理与可解释的ML工具联系在一起的框架.
- 护士必须具备安全地利用AI/ML,减轻偏见,并坚持在技术增强护理中的道德责任.
- 对于护士来说,一个结构化的方法是必不可少的,以有效和道德地将AI / ML纳入他们的实践.
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