Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

TL;DR

This paper reviews the current state, applications, and challenges of AI-enabled remote patient monitoring systems, highlighting their transformative impact on healthcare through early detection, personalization, and behavior analysis.

Contribution

It provides a comprehensive overview of AI technologies in RPM, discussing benefits, challenges, and future trends in integrating AI with IoT, cloud, fog, edge, and blockchain in healthcare.

Findings

AI-enabled RPM detects early health deterioration

Personalized monitoring via federated learning

Behavior pattern learning with reinforcement learning

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