Open AccessAI-Enhanced Threat Intelligence in Remote Patient Monitoring Systems: A Survey on Recent Advances, Challenges and Future Research Directions
Author(s) -
Jolly Trivedi,
Mohammad Tahir,
Jouni Isoaho
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3572626
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The adoption of IoT devices for Remote Patient Monitoring (RPM) systems has emerged as a new attack surface and has resulted in an increased risk of cyber-attacks. Besides the traditional security mechanism for RPM systems, Artificial Intelligence (AI), has emerged as a crucial technology to address the security issues in IoT-enabled RPM systems. In this regard, this article provides a survey of the security challenges of RPM systems and discusses the role of AI in strengthening the security of RPM systems through advanced threat intelligence capabilities. The survey analyses 110 research papers from leading databases related to AI, the security of RPM systems, anomaly detection, architectural solutions, and existing RPMs. This review of AI-enhanced Threat Intelligence in RPM systems highlights the research gaps, which include the necessity of comprehensive end-to-end architectures for maintaining security and privacy in the RPM systems. The survey reveals traditional RPM systems are vulnerable to data breaches due to their centralized architecture. AI-powered threat intelligence enhances RPM security significantly by identifying anomalies in patient data, enabling continuous monitoring and early detection of threats. Federated Learning allows for decentralized training of AI models, providing both security and privacy. However, challenges like the explainability of AI models persist, which need continued innovation. This survey paper suggests integrating AI Enhanced Threat Detection as a Service (TDaas) that implements Federated Learning (FL) to transform the existing RPM security system and ultimately contribute to a secure and reliable Threat Detection system in Healthcare. This literature review lays out a roadmap for future research in the area of AI-driven threat intelligence security for RPM systems and offers insights for developing resilient healthcare infrastructures.