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.
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.
The Internet of Medical Things (IoMT) provides the capabilities of continuous real-time monitoring of patients using connected devices as well as generating a great deal of healthcare data; these are the benefits of IoMT. According to multiple sources, machine learning (ML) and deep learning (DL) have been used in the medical field for many years, and there are also issues surrounding these technologies that impact their ability to support clinical decision-making. These include issues related to scalability, interoperability, latency, explainability of the model output, and cybersecurity (WHO, 2021; Esteva et al., 2019; Topol et al., 2019). In this chapter, a hybrid classical-quantum framework is proposed for use in real-time patient monitoring, which integrates Quantum Intelligence (QI), Quantum ML (QML), Edge Computing (EC), and the IoMT. In addition, the authors provide examples of current use cases as well as practical recommendations to support the adoption of secure, scalable, and intelligent healthcare monitoring systems.
The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease management. However, this interconnected ecosystem faces significant vulnerabilities with the advent of quantum computing, which threatens to break existing encryption standards protecting sensitive patient data in IoT-enabled medical devices. This chapter examines the quantum threat to healthcare IoT security, highlighting the potential impacts of compromised encryption, including privacy breaches, device failures, and manipulated medical records. It introduces post-quantum cryptography (PQC) and quantum-resistant techniques like quantum key distribution (QKD), addressing their application in resource-constrained healthcare IoT devices such as pacemakers, monitoring tools, and telemedicine systems. The chapter further explores the challenges of integrating these solutions and reviews global efforts in mitigating quantum risks, offering insights into suitable PQC primitives for various healthcare use cases.
The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered.
The emergence of 5G networks has revolutionized healthcare delivery by enabling communication with ultra-low latency, massive connectivity, and quick access to IoT devices' medical data. The digitalization of healthcare has, however, introduced privacy, data security, and scalability as major challenges, and the issues have been worsened by the rapid digitalization of the healthcare industry. The current review paper is a comprehensive evaluation of the role of Quantum Machine Learning (QML) as a paradigm for data handling with privacy preserved in the healthcare ecosystems supported by 5G. It collects advancements in quantum cryptography, Quantum Key Distribution (QKD), federated learning, and blockchain technology to explore how the combining of such technologies will enhance the confidentiality, interoperability, and real-time decision-making of the data. The review arranges and compares the state-of-theart research of 2019-2025, in which the trends, architectures, and security protocols that link classical and quantum methods in healthcare analytics become visible. It also addresses the main hurdles like quantum hardware limitations, interoperability gaps, and the tuning of federated models, and it indicates potential future research paths for the development of healthcare infrastructure that is scalable, reliable, and quantum-resistant. In conclusion, this paper gives a thorough understanding of how quantum intelligence can alter the data security and privacy landscape in 5G-enabled smart healthcare systems.
The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This article explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this article aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes.
The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,