基于云的AI框架用于慢性疾病诊断:与最近的AI-IoMT模型 (2020-2025) 进行更新的比较分析
1Department of Physics, School of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Frontiers in medical technology
|February 9, 2026
概括
支持人工智能的医疗物联网 (IoMT) 系统对慢性疾病具有很高的诊断准确性. 雾计算减少了延迟时间,但可以增加能源使用,强调了需要平衡的系统设计.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 计算机网络 计算机网络
背景情况:
- 慢性疾病需要持续监测和及时的临床反应.
- 医疗物联网 (IoMT) 将患者传感与雾和云计算相结合,以实现高效的数据处理.
- 人工智能驱动的IoMT旨在增强诊断能力和系统性能.
研究的目的:
- 审查在2020-2025年期间发布的支持AI的IoMT系统.
- 综合诊断性能和网络服务质量 (QoS) 指标.
- 为未来的AI-IoMT部署提供设计建议.
主要方法:
- 按照PRISMA 2020指南进行系统的文献审查.
- 对数据库记录进行选以确定相关的初级研究.
- 针对糖尿病和心血管疾病的六个IoMT-Fog-Cloud框架进行了集中综合.
主要成果:
- 糖尿病IoMT系统通过深度学习实现~95-96%的准确性;心血管系统在受控环境中报告>99%.
- 雾层处理减少了生物信号的端到端延迟,但可能会增加能源需求.
- 提取的指标涵盖诊断性能和系统/网络 QoS,用于可重复的比较.
结论:
- 人工智能IoMT系统为慢性疾病管理提供了有前途的诊断能力.
- 从雾计算中获得的性能收益与能源考虑的平衡至关重要.
- 标准化指标和透明的报告对于未来的AI-IoMT研究和部署至关重要.
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