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 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 growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.
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.
Quantum computing, due to its capacity to do calculations that surpass the capabilities of classical computers, presents revolutionary solutions for drug development and genome research. By analyzing extensive datasets, Machine Learning improves predictive accuracy, enabling personalized treatment and enhancing clinical decision-making. A critical function of the Internet of Things is to facilitate ongoing patient monitoring using interconnected devices, therefore assuring prompt interventions and effective healthcare delivery. The chapter offers a framework for Quantum Machine learning and IoT for drug discovery in oncology. A detailed comparative analysis of the existing Quantum ML algorithms and practical uses of various technologies in the healthcare field, emphasizing their potential for interplay, has been discussed. Furthermore, it explores the obstacles that these technologies encounter, including data protection, ethical constraints, and technical scalability. In this study, we present comprehensive case studies in the fields of oncology and chronic disease management to illustrate the practical implications of these advancements. This chapter advocates making use of the combined abilities of these technologies to develop a healthcare ecosystem that is more efficient, accurate, and focused on the needs of people. This chapter examines how these cutting-edge technologies can seamlessly work together to solve important healthcare issues like correct disease diagnosis, personalized treatment, and constant patient tracking.
Quantum-Enhanced Artificial Intelligence (QAI) integrates quantum computing with advanced AI to tackle the increasing complexity of healthcare data. The chapter discusses the impact of QAI in medical diagnostics, precision medicine, drug discovery and clinical decision support systems. It also explores the use cases in the Internet of Medical Things (IoMT), digital twins, and predictive healthcare analytics. In addition, current challenges, ethical issues and future directions are identified, illustrating the possibilities of QAI to empower next-generation intelligent medical systems to provide faster, more accurate and personalized healthcare.
The field of healthcare management is currently undergoing substantial changes due to integration with emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), big data analytics, Internet of Medical Things (IoMT), and Quantum Computing (QC). Although ML has already proved its effectiveness for use in healthcare management, diagnosing diseases, monitoring patients, managing healthcare resources, discovering drugs etc. Quantum computing appears to be the advance approach that will be able to resolve variety of complicated optimization problems and tasks that remain unsolvable within the scope of classic computers. Combining QC and ML has a great potential of significantly impacting the realm of healthcare management. The present study is intended to give a comprehensive overview of existing research into Quantum Mechanics Learning in the scope of healthcare management. The research includes reviewing recent literature, algorithms, applications, implementation strategies, challenges, and further research perspectives.
This chapter develops an integrated framework for quantum-enhanced federated digital twins in Internet of Medical Things (IoMT)-based smart healthcare systems. It explains how continuous sensing, edge intelligence, federated learning, digital twin modeling, and near-term hybrid quantum-classical machine learning can be combined to support privacy-preserving predictive and personalized medicine. The chapter argues that the value of quantum-enhanced healthcare should not be framed as immediate quantum superiority, but as a disciplined pathway for testing quantum kernels, variational circuits, quantum-inspired optimization, and hybrid classifiers against strong classical baselines. It presents a layered architecture, clinical use cases, security safeguards, implementation challenges, and research directions. The chapter provides a clinically grounded and ethically cautious roadmap for researchers, healthcare AI developers, hospital leaders, and policymakers seeking trustworthy intelligent healthcare without centralizing sensitive patient data.