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.

中文翻译：

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使用人工智能进行远程患者监护：现状、应用和挑战

人工智能 (AI) 在医疗保健领域的应用正在迅速增长。远程患者监护 (RPM) 是一种常见的医疗保健应用程序，可帮助医生在偏远地区监控患有慢性或急性疾病的患者、居家护理的老年人，甚至是住院患者。手动患者监护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，需要皮肤接触才能监测健康状况。本研究旨在全面审查 RPM 系统，包括采用的先进技术、AI 对 RPM 的影响、AI 支持的 RPM 的挑战和趋势。本综述探讨了使用云、雾、边缘和区块链技术的物联网可穿戴设备和传感器支持的以患者为中心的 RPM 架构的优势和挑战。AI 在 RPM 中的作用范围从身体活动分类到慢性病监测和紧急情况下的生命体征监测。该审查结果表明，支持 AI 的 RPM 架构已经改变了医疗保健监测应用程序，因为它们能够检测患者健康状况的早期恶化，使用联合学习个性化个体患者健康参数监测，并使用强化学习等技术学习人类行为模式。本综述讨论了将 AI 应用于 RPM 系统和实施问题的挑战和趋势。