针对的可穿戴人工智能:范围审查
Sarah Aziz1, Amal A M Ali2, Hania Aslam1
1AI Center for Precision Health, Weill Cornell Medical College in Qatar, Education City, Street 2700, Doha, 24144, Qatar, 974 4492 8827.
Journal of medical Internet research
|October 31, 2025
概括
可穿戴的人工智能 (AI) 为管理提供了有前途的发作检测. 未来的研究需要标准化验证和开放数据,以获得临床影响.
科学领域:
- 医疗设备和可穿戴技术
- 医疗保健中的人工智能
- 神经学和病管理管理
背景情况:
- 影响全球5000万,需要持续监测,以有效管理.
- 可穿戴的人工智能技术利用生理信号和机器学习来检测和预测发作.
- 需要对用于的可穿戴人工智能的进展和挑战进行全面的概述.
研究的目的:
- 综合审查和绘制关于人工智能驱动的可穿戴技术的文献.
- 识别设备特性,人工智能方法,生物信号测量和验证方法.
- 确定用于管理的可穿戴人工智能领域的研究缺口.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 在六个数据库 (Scopus,MEDLINE,Embase,ACM,IEEE,Google Scholar) 进行系统搜索,截至2023年12月.
- 包括使用非侵入性可穿戴设备治疗的AI算法研究;不包括非可穿戴或体内设备.
主要成果:
- 67项研究符合纳入标准,自2021年以来,研究显著增长.
- 活动,心血管,大脑活动和皮肤导电性是监测的主要生物信号.
- 支持矢量机器,随机森林和CNN是常见的AI模型;专注于发作检测而不是预测.
- 报告指出,临床验证有限,闭源数据占主导地位.
- 离开一个和k倍交叉验证是常见的,视频EEG作为主要参考.
结论:
- 可穿戴的人工智能显示出对实时监测和早期发作检测的承诺.
- 验证方法的标准化和开放的数据交换对于临床影响至关重要.
- 需要开发节能算法,以便在现实世界中部署可穿戴设备.
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