mlsanityData to 14 Oct 2026

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

arXiv 2301.10009 ↗First posted 19 Jan 2023cs.CY cs.AIcited by 14

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Abstract (arXiv)

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

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 17 of its 177 references that are in the map. First posted on arXiv 19 Jan 2023.

Builds on

17 of 177 references.

References · 17

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5 more
  1. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
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Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 5.

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  2. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  3. 2023Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjectscited by 2
  4. 2024An AI-Driven Multimodal Smart Home Platform for Continuous Monitoring and Assistance in Post-Stroke Motor Impairmentcited by 1
  5. 2025Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Reviewcited by 1
  6. 2025New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistancecited by 1
  7. 2026A longitudinal geospatial multimodal dataset of post-discharge frailty, physiology, mobility, and neighborhoodscited by 1
  8. 2026A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Educationcited by 1
  9. 2023Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samplescited by 0
  10. 2024Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computingcited by 0
  11. 2025Are AI Machines Making Humans Obsolete?cited by 0
  12. 2026Hierarchical adaptive control for real-time dynamic inference at the edgecited by 0
2 more
  1. 2026AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoringcited by 0
  2. 2026Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reductioncited by 0