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.
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.
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 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 growing digitalization of medical diagnostics has resulted in massive amounts of complicated data, including medical imaging and genomic sequences, clinical writing and real-time patient monitoring. Classical machine learning (CML) has achieved amazing success in evaluating such data. But its computational restrictions hinder scalability and efficiency when dealing with high-dimensional biomedical problems. Quantum machine learning (QML) combines the principles of quantum computing (QC) with advanced learning algorithms to offer a transformative paradigm for digital healthcare. This paper provides a systematic overview of QML foundations including quantum data encoding (QDC), variational quantum circuits (VQC), kernel methods, and hybrid quantum classical models. This paper also focuses on their applications in medical imaging, genomics, natural language processing (NLP) for electronic health records, drug discovery and healthcare security. We present comparative insights between classical and quantum approaches such as slow processing of high-dimensional data, limited scalability and inefficiency in complex optimization problems. This review also emphasizes the emerging directions towards quantum-based personalized digital healthcare approaches. By combining medical science with quantum QML has the potential to revolutionize the future of precision diagnostics, treatment optimization and healthcare data security. This study also provides a valuable resource for those interested in quantum computing and researchers who want to stay updated on the fast-growing area.
The merger between Quantum Computing (QC) and Machine Learning (ML) will enable health analytics to be experienced in a way never seen before with unmatched efficiency and power. Conventional methods of healthcare data processing have not been able to work with very big and complex data sets that are related to medical imaging, genomics, and real-time tracking of the patients. Quantum Machine Learning (QML) has an advantage, as it uses new algorithms to work with such multidimensional data with much better accuracy, speed, and predictive power. This paper presents the essential contribution of QML to healthcare analytics, such as supporting diagnosis, to personalized medicine and predictive modelling. This quantum enhanced deep learning models will achieve what is currently being done at the larger scale in the field of medicine at a faster rate a thousand times, through earlier detection of diseases like cancer, Alzheimer disease and heart diseases. In drug discovery, quantum algorithms decrease the simulation of molecular interactions down to the atomic scale, which saves a substantial amount of time and cost of research. Security is, nevertheless, a massive issue since future developments in quantum theory will generate quantum-wise systems that will bring misfortunes to conventional encryption solutions adopted within healthcare systems. The implications of post-quantum cryptography that ensures the strong data privacy and safe patient records in quantum-powered ecosystem are discussed. Scalability obstacles, systems creation, and integration with traditional ML models, however, exist. Real-world applications and future perspectives on the quantum-enhanced healthcare analytics upon which plans have already been laid are presented. Looking ahead, QML will build in the transition of healthcare systems toward analytics frameworks faster, much more precise, and enhanced secure, heralding in the day of precision medicine early diagnostics and a new age for healthcare innovation.
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.
Quantum computing with AI-driven medical technologies represents an absolute frontier of transformation in precision medicine, diagnostics, and drug discovery. This chapter explores the intersection of quantum computing and artificial intelligence in transforming complex medical challenges, namely in big data analysis, acceleration in molecular simulations, and personalizing treatment plans. Quantum algorithms can scale exponentially beyond AI's capabilities in the breakthrough of genomic analysis, radiological interpretation, and predictive modeling for patient outcomes. The focus of the chapter will be on state-of-the-art achievements in quantum machine learning, possible applications in healthcare areas, and their future prospects for the revolutionizing of medical imaging, optimization of clinical trials, and real-time data processing on wearable devices. They confront the computational limits of classical systems and offer new ground for significant progress in both medical research and health care delivery by solving problems that have until now been intractable.