  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
