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.
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.
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.
The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes. For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.
This Healthcare is a critical sector were integrating emerging technologies like Quantum Computing (QC) could transform medical research, diagnosis, and treatment. Despite its achievements, significant obstacles still exist in healthcare, including disease detection, ineffective drug discovery procedures, and an excessive amount of data that is too big for conventional computing systems to analyze. The processing and analysis of medical data could be entirely transformed by applying QC in healthcare, resulting in more precise diagnoses and enhanced efficiency. This paper examines key milestones at the intersection of quantum computing and the healthcare sector, highlighting advancements in drug development, processing data, and medical diagnostics that have led to improved patient outcomes. This paper examines challenges within the field, specifically focusing on data privacy and security, scaling, hardware constraints, and ethical considerations. This paper examines the potential for quality control in light of emerging innovations within the healthcare sector.
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
Quantum computing, a paradigm rooted in quantum mechanics, is poised to revolutionize healthcare by addressing computational challenges that classical systems cannot solve efficiently. With their ability to process vast datasets through superposition and entanglement, quantum computers offer new approaches to drug discovery, diagnostics, genomics, and personalized medicine. This paper examines the fundamental principles of quantum computing and its application in the healthcare domain. It examines real-world use cases such as quantum machine learning for biomarker detection, molecular simulation for drug development, and quantum optimization for healthcare logistics. Additionally, the study outlines the technological, ethical, and infrastructural challenges that must be overcome for quantum healthcare solutions to achieve full clinical integration. By assessing current research, experimental applications, and the trajectory of quantum advancements, this paper provides a roadmap for the responsible and effective deployment of quantum computing in modern healthcare systems. Keywords: Quantum Computing, Healthcare Technology, Quantum Machine Learning, Personalized Medicine, Drug Discovery, Biomarker Detection.
The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.