Introduction Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in health and medicine. However, the understanding of these technologies in early to late stages of development is limited. This study aimed to investigate the emerging applications of quantum technologies in health care, with a focus on quantum computing, diagnostics, and therapeutics. Methods Horizon scanning researchers, in collaboration with information specialists and with steering from subject matter experts at National Health Service England, developed search strategies for identifying relevant emerging and established quantum applications in healthcare domains such as drug design, personalized medicine, diagnosis assistance, and treatment or interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. Primary and secondary sources were reviewed systematically using a combination of traditional methods and the Innovation Observatory’s in-house semi-automated Search Companion for Advanced News Article Retrieval (SCANAR) tool to capture non-traditional soft intelligence sources. Results The scan identified 116 quantum technologies in health care, with magnetoencephalography (MEG), quantum dots, and superconducting quantum interference devices (SQUIDs) being the most frequently used for brain mapping, imaging, and cardiac diagnostics. Over half (54%) of these technologies assist in diagnosis, while others are used in drug design (15%), treatment (15%), and precision medicine (13%). Quantum computing applications (28%) and quantum dots (24%) were the most common applications identified. Non-commercial trials dominated (88%), reflecting a focus on advancing care. Additionally, 27 percent of the technologies incorporate artificial intelligence, mainly for personalized medicine and imaging diagnosis. Conclusions Quantum technologies in health care are advancing areas of diagnostics, personalized medicine, and therapeutics. Diagnostics are the most popular area, most frequently using technologies such as MEG, SQUIDs, and quantum dots. Quantum computing algorithms accelerate drug discovery and precision medicine. Although promising, integrating quantum technologies into health care faces challenges in infrastructure demands and regulatory frameworks, and requires professional training in quantum literacy.
Introduction Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in health and medicine. However, the understanding of these technologies in early to late stages of development is limited. This study aimed to investigate the emerging applications of quantum technologies in health care, with a focus on quantum computing, diagnostics, and therapeutics. Methods Horizon scanning researchers, in collaboration with information specialists and with steering from subject matter experts at National Health Service England, developed search strategies for identifying relevant emerging and established quantum applications in healthcare domains such as drug design, personalized medicine, diagnosis assistance, and treatment or interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. Primary and secondary sources were reviewed systematically using a combination of traditional methods and the Innovation Observatory’s in-house semi-automated Search Companion for Advanced News Article Retrieval (SCANAR) tool to capture non-traditional soft intelligence sources. Results The scan identified 116 quantum technologies in health care, with magnetoencephalography (MEG), quantum dots, and superconducting quantum interference devices (SQUIDs) being the most frequently used for brain mapping, imaging, and cardiac diagnostics. Over half (54%) of these technologies assist in diagnosis, while others are used in drug design (15%), treatment (15%), and precision medicine (13%). Quantum computing applications (28%) and quantum dots (24%) were the most common applications identified. Non-commercial trials dominated (88%), reflecting a focus on advancing care. Additionally, 27 percent of the technologies incorporate artificial intelligence, mainly for personalized medicine and imaging diagnosis. Conclusions Quantum technologies in health care are advancing areas of diagnostics, personalized medicine, and therapeutics. Diagnostics are the most popular area, most frequently using technologies such as MEG, SQUIDs, and quantum dots. Quantum computing algorithms accelerate drug discovery and precision medicine. Although promising, integrating quantum technologies into health care faces challenges in infrastructure demands and regulatory frameworks, and requires professional training in quantum literacy.
ABSTRACT Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in drug discovery, medical diagnosis, precision medicine, and other therapeutic interventions. However, the research on emerging quantum technologies at early to late stages of development for healthcare applications is limited. The main objective of this study was to identify emerging quantum technologies such as quantum computing, diagnostics, and therapeutics, with a focus on specific applications within healthcare, such as drug discovery, diagnosis assistance, precision medicine, and treatment interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. The analysis revealed 116 quantum technologies such as computing algorithms, therapeutics, sensors, and imaging applications that are currently in development or already in the market. Diagnosis assistance‐related technologies, including technologies such as magnetoencephalography and quantum dots, constituted the majority of the technologies, while quantum computing‐related machine learning and algorithms were significant in drug discovery and precision medicine applications. The integration of quantum technologies into healthcare faces challenges such as infrastructure demands, regulatory frameworks, and the need for professional training. However, with ongoing advancements, quantum technologies are uniquely positioned to revolutionize diagnostic accuracy, computational capacity for drug design, and precision medicine. This horizon scan highlights the current innovation landscape of emerging quantum technologies in healthcare and the challenges in facilitating the integration of these technologies into healthcare systems.
Medical imaging, a cornerstone of modern diagnostics that underpins early disease detection and personalized therapy, continues to confront fundamental physical limitations inherent to conventional modalities, including suboptimal sensitivity and specificity, limited resolution, and ionizing radiation risks. Quantum imaging, leveraging principles such as entanglement, superposition, and quantum sensing, has emerged as a transformative paradigm with the potential to transcend these limitations. This review systematically summarizes the physical principles, technological advancements, and major clinical applications of quantum technologies in medical imaging. We elaborate on several cutting-edge modalities, including quantum dot probes for targeted imaging and surgical navigation, wearable magnetoencephalography facilitated by optically pumped magnetometers, quantum-enhanced magnetic resonance imaging with nanoscale resolution, quantum optical coherence tomography with dispersion cancellation, photon-counting computed tomography for superior spectral imaging, and nuclear medicine imaging that can visualize the molecular microenvironment. Additionally, the roles of quantum computing and quantum artificial intelligence in accelerating and optimizing the image analysis workflow are also discussed. Furthermore, we analyze the technical bottlenecks and translational barriers that currently hinder the widespread clinical implementation of these emerging technologies. Finally, we outline promising future research directions to accelerate clinical translation, underscoring the transformative potential of quantum medical imaging to advance precision diagnostics and personalized medicine.
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.
The use of quantum computing1 in healthcare represents a significant shift with the potential to revolutionize medical research, diagnostics, and treatment. Quantum computing can process and analyze large datasets much faster than classical computing, offering new opportunities in healthcare. This paper examines the various ways quantum computing can be applied in healthcare, such as in drug discovery, genetic research, personalized medicine, and complex disease modeling. By using quantum algorithms, researchers can identify new treatment targets and simulate the effects of drugs on human biology in ways that were previously impossible, which can reduce the time and cost of bringing new drugs to market. Quantum computing also allows for analyzing large genomic datasets2, which can help tailor treatments to individual genetic profiles. Additionally, this technology helps in modeling complex biological systems and disease processes, providing insights into the mechanisms of diseases at a molecular level and enabling the development of more effective treatments. However, integrating quantum computing into healthcare comes with challenges such as technical limitations, the need for specialized expertise, data security concerns, and ethical considerations. This paper provides a comprehensive overview of the potential applications of quantum computing in healthcare, addresses the associated challenges, and discusses the future directions of this growing field, emphasizing the importance of interdisciplinary collaboration in fully utilizing quantum technologies in medicine. In recent years, quantum computing has rapidly advanced and attracted significant interest from academia and industry due to its unique ability to process information differently, resulting in previously unattainable computational capabilities. Despite its potential, the full impact of quantum computing on healthcare has not been thoroughly explored. This survey paper provides a systematic analysis of quantum computing's various capabilities in improving healthcare systems, focusing on its potential to transform compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing4, medical imaging5, and operational optimization. Through a comprehensive review of existing literature, we have developed taxonomies6 covering different aspects, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, offering a comprehensive view of the quantum computing paradigm for healthcare. Our survey aims to assist new and experienced researchers in quantum computing and healthcare by helping them comprehend the current research landscape, identify potential opportunities and challenges, and make informed decisions when developing new architectures and applications for quantum computing in healthcare
Abstract Quantum computing and artificial intelligence (AI) are converging to reshape the future of healthcare. Modern medicine generates vast and complex datasets from imaging, genomics, wearable sensors, and real-time clinical records. While AI has already demonstrated significant potential in diagnostics, drug discovery, and personalised medicine, its progress is often constrained by high computational demand, limited interpretability, and energy inefficiency. Quantum computing offers a fundamentally different paradigm, using qubits, superposition, and entanglement to process high-dimensional data and explore solution spaces in parallel. This synergy-quantum-enhanced AI or quantum machine learning (QML)promises transformative capabilities across the healthcare continuum. This narrative review explores the foundations of quantum computing, the evolution of AI in medicine, and the points where these technologies intersect. Applications span a range of biomedical domains. In radiology and pathology, quantum-assisted classifiers can enhance both the speed and interpretability of image analysis. In drug discovery and genomics, variational quantum eigensolvers and hybrid quantum–classical models may accelerate molecular simulations and genome-wide association studies. In personalized medicine, quantum kernel methods enable more precise phenotype clustering and improve the accuracy of treatment response predictions. Operationally, quantum-AI integration also supports hospital logistics, ICU resource allocation, and supply-chain resilience. Despite these opportunities, significant challenges remain, including hardware limitations, data encoding bottlenecks, interpretability deficits, and regulatory ambiguity. Ethical concerns around privacy, equity, and compliance with frameworks such as HIPAA and GDPR must also be addressed. By mapping opportunities and risks, this review underscores that quantum-AI is no longer speculative but an emerging reality. Harnessing its potential requires multidisciplinary collaboration to translate qubits and algorithms into tangible improvements in patient outcomes, clinical trust, and system efficiency.
Quantum computing, an interdisciplinary area that processes data in fundamentally different ways, has recently grown and attracted a lot of attention from academics and businesses alike because of its potential to achieve computational powers that were previously unimaginable. Quantum computing's potential in the medical field has not been thoroughly explored as yet. Computing at the quantum level has the potential to revolutionize computationally intensive healthcare applications such as personalized medicine, drug discovery, medical imaging, DNA sequencing, and operational optimization. This review article provides the first all- encompassing examination of the many applications of quantum computing to healthcare systems, focusing on these specific domains. Many different areas are covered by our taxonomies, such as foundational and enabling technologies, applications, needs, architectures, security, open problems, and potential avenues for future study. The outcome of this thorough literature study is an all-encompassing perspective on the quantum computing paradigm as it pertains to healthcare. Synthetic intelligence (AI) and quantum computing are about to converge, and this article delves into how that can change the face of healthcare. In this study, we take a look at how quantum physics might help us understand how to improve AI for faster drug development, more precise clinical diagnostics, and more tailored treatment plans. The investigation probes the means by which molecular-level quantum simulations can represent complicated organic processes, enabling the unprecedented prediction of treatment interactions and the design of medicine. Also covered in the article is the idea of using quantum sensors in conjunction with AI to provide better clinical imaging that can pick up on more widespread problems. Highlighting its role in bringing in a new technology of personalized medicinal drug and efficient drug improvement, this synergy is discussed for its transformational power.