Abstract
The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends
Abstract (translated)
人工智能(AI)在医疗保健中的广泛应用正在快速发展。远程患者监测(RPM)是常见的医疗保健应用程序之一,协助医生在远程地点监测患有慢性病或急性病的患者、照顾老年人甚至是住院治疗的患者。手动患者监测系统的可靠性取决于工作人员的时间管理,这取决于他们的工作量。传统的患者监测涉及侵入性方法,需要皮肤接触来监测健康状况。本研究旨在对RPM系统进行综合性评估,包括采用先进技术、AI对RPM的影响、以及AI驱动的RPM面临的挑战和趋势。该研究探讨了利用物联网可穿戴设备和传感器使用云计算、雾、边缘和区块链技术实现患者中心化的RPM架构的好处和挑战。AI在RPM中的应用作用涵盖了运动分类、慢性病监测和紧急情况下的生命支持系统 vital signs监测。该研究结果表明,AI驱动的RPM架构已经改变了医疗保健监测应用程序,因为其能够早期检测患者健康状况的恶化,使用分布式学习个性化个体患者健康参数监测,以及使用强化学习等技术学习人类行为模式。该研究讨论了将AI应用于RPM系统和实施问题面临的挑战和趋势。基于这些挑战和趋势,分析了AI在RPM应用程序中的未来方向。
URL
https://arxiv.org/abs/2301.10009
