Quantum-Assisted Iot Architecture for Real-Time and PrivacyPreserving Healthcare Systems

Abstract

The use of Remote Patient Monitoring (RPM) through IoT based architectures on the edge and in the cloud is growing rapidly. These systems increase access; however, the drawbacks are high latency, vulnerability to a variety of security threats, and the lack of a framework for real-time processing of complex biomedical signals. This article is a systematic review of quantum-enabled Remote Patient Monitoring (RPM) systems published between 2014-2020, using peerreview literature within the domains of IoT, Internet of Medical Things (IoMT), Artificial Intelligence (AI), edge and cloud-computing, security frameworks, and medical signal fusion. The review indicates that Quantum Internet of Things (Q-IoT) addresses the current limitations of RPM by utilizing quantum communications, including Quantum Key Distribution (QKD) and Quantum-assisted machine learning, to facilitate secure and intelligent medical analytics. The results show that quantum communications are highly secure for data transmission and Quantum-assisted machine learning improves diagnostics and decision-making capabilities. The systematic review also identified several key challenges to the adoption of Q-IoT, including the current lack of quantum hardware, the challenges associated with integrating Q-IoT with legacy medical devices, the importance of privacy, scalability, and energy efficiency. The review concludes with recommendations for future research to support the establishment of secure, low-latency, and reliable QIoT based RPM systems in next-generation smart healthcare.