基于人工智能的诊断系统在急诊室的临床影响:系统性审查
Abubaker Zakria Ahmed Abdalhalim1, Sheimaa Nasreldein Nureldaim Ahmed2, Ahmed Mohamed Dawoud Ezzelarab3
1Emergency Medicine, Rustaq Hospital, Rustaq, OMN.
Cureus
|July 11, 2025
概括
人工智能 (AI) 可以提高急救部门 (ED) 的分拣效率和准确性,减少文件处理时间和错误分拣率. 然而,需要进一步的多中心研究来解决局限性并确保安全实施.
科学领域:
- 紧急医疗 紧急医疗
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 紧急诊所 (ED) 面临着越来越多的患者数量和资源限制,需要优化选过程.
- 人工智能 (AI) 提出了一个潜在的解决方案,用于提高ED设置中的分拣精度和效率.
- 人工智能在ED诊断中的实际临床影响仍然没有得到充分的描述.
研究的目的:
- 系统地审查和综合有关基于人工智能的ED分拣系统性能和影响的证据.
- 将人工智能驱动的分拣结果与传统方法进行比较.
- 在ED分类中识别AI的局限性和实施挑战.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 在主要数据库 (PubMed,Embase,Web of Science,IEEE Xplore) 搜索了2020-2025年的研究.
- 包括六项报告定量结果的研究;使用调整的 QUADAS-2 评估偏差风险;采用叙事合成.
主要成果:
- 人工智能在减少分拣时间和提高文档准确性方面表现出潜力.
- 语音-AI系统显示19%更快的文档;机器学习将错误分类率降低了0.3-8.9%.
- 局限性包括不足的风险,可变的准确性,单一中心的优势,以及工作流集成的挑战.
结论:
- 基于人工智能的分类系统有望提高ED效率,但需要多中心验证和标准化报告.
- 关键的差距包括以患者为中心的结果,公平性和长期影响.
- 未来的开发应该专注于可解释的算法,临床医生的参与以及安全实施的伦理框架.
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