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.
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.
The major recent cutting-edge technologies such as quantum computing (QC), artificial intelligence (AI), and machine learning (ML) have well enough potential to utterly revolutionize the healthcare industry. Specifically, quantum AI and quantum machine learning (QML) techniques are more powerful and can promptly transform the healthcare industry across the world. Healthcare industry already makes extensive use of AI, from evaluating medical images, such as electronic medical records (EMRs), to creating personalized treatment regimens for patients. Even though quantum computing is still in its primary phases, it has the ability to tackle difficult or complex problems far more quickly than conventional or traditional computers. The advanced AI algorithms used in healthcare diagnostics and medical research, where analyzing enormous amounts of data for frequently minute details is required, benefit greatly from quantum computers’ ability to process vast amounts of data quickly and intuitively. This book chapter presents a comprehensive exploration of quantum artificial intelligence and machine learning techniques in revolutionizing healthcare industries. It also explores how quantum AI and ML techniques are used in drug design, medical image analysis, healthcare management, genomic analysis, and precision medicine.
The healthcare sector is undergoing a significant transformation driven by advances in Artificial Intelligence, Big Data Analytics, Internet of Medical Things, Cloud Computing, and Digital Health technologies. While these technologies have enabled intelligent healthcare services, the increasing complexity, volume, and heterogeneity of healthcare data pose substantial computational challenges for conventional systems. To address these limitations, Quantum Computing, has emerged as a promising solution. This chapter presents a comprehensive review of the integration of quantum computing and intelligent technologies in smart healthcare systems. It examines the fundamental concepts of quantum computing and explores their applicability in healthcare environments. It also discusses quantum machine learning models for diagnostic support and personalized healthcare. It further investigates emerging applications including drug discovery, genomic analysis and medical image reconstruction. Finally, the challenges, implementation barriers, and future directions are also presented.
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.
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.
The healthcare industry is experiencing a significant digital transformation due to the proliferation of electronic health records, medical imaging technologies, wearable sensors, genomic sequencing, and Internet of Medical Things (IoMT) devices. These technologies produce enormous quantities of heterogeneous and high-dimensional data that hold valuable insights for disease diagnosis, prognosis, treatment planning and population health management. Conventional machine learning and deep learning methods have achieved significant success in extracting patterns from medical data, but their performance is increasingly limited by computational complexity, scalability limitations, and the increasing dimensionality of healthcare datasets. Quantum Machine Learning (QML) is a young interdisciplinary field, which integrates quantum computing with machine learning methods, and offers a promising way to overcome these challenges and enable more powerful intelligent healthcare systems.
Quantum Machine Learning (QML) is an emerging discipline at the intersection of quantum computing and artificial intelligence, providing new approaches to address the increasing computational and analytical challenges in healthcare. This chapter explores how quantum principles, such as superposition, entanglement, and interference, can enhance the efficiency and accuracy of machine learning when applied to complex biomedical data. It presents an overview of the fundamental concepts of QML, compares its capabilities with those of traditional machine learning systems, and examines important algorithmic frameworks, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms. Practical examples are explored across several healthcare domains—drug discovery, medical imaging, genomic research, patient monitoring, and hospital resource management—showing how quantum methods are beginning to improve data interpretation and clinical decision-making. The chapter also outlines current limitations related to hardware capacity, data preparation, interpretability, and regulatory standards, while emphasizing ethical and cybersecurity considerations that accompany quantum-enabled systems. Finally, it looks ahead to a future in which accessible quantum computing, open-source development, and collaboration among scientists, clinicians, and technologists drive a new generation of personalized, secure, and data-driven healthcare.