The development of quantum technology has advanced rapidly to become a critical transformative device for use in medical diagnosis and treatment, surpassing the boundaries of normal methods. By applying principles such as superposition, complexity, and quantum tunnelling in QDs, it is now possible to produce highly sensitive equipment that can diagnose diseases at their earliest stages. Quantum sensors enable tracking physical characteristics with nuclear accuracy, helping detect molecular alterations before symptoms appear. Quantum computing increases diagnostic capabilities by rapidly processing large therapeutic datasets, enabling the recognition of various patterns and gene sequences and the immediate suggestion of personalised treatments. Combining QD with IoT and AI-based healthcare systems creates an ecosystem that enables continuous monitoring, future-state analysis, and instant decision-making. This kind of cooperation is expected to yield good results in the case of neurodegenerative and infectious diseases. However, implementing QD in medical diagnostics faces several significant obstacles, including high growth costs, scalability issues, technical immaturity, and regulatory gaps. Nevertheless, further work is going on despite these limitations. The current chapter will focus on the technical basis, present uses, limitations, and future of quantum technology in diagnosis.
The development of quantum technology has advanced rapidly to become a critical transformative device for use in medical diagnosis and treatment, surpassing the boundaries of normal methods. By applying principles such as superposition, complexity, and quantum tunnelling in QDs, it is now possible to produce highly sensitive equipment that can diagnose diseases at their earliest stages. Quantum sensors enable tracking physical characteristics with nuclear accuracy, helping detect molecular alterations before symptoms appear. Quantum computing increases diagnostic capabilities by rapidly processing large therapeutic datasets, enabling the recognition of various patterns and gene sequences and the immediate suggestion of personalised treatments. Combining QD with IoT and AI-based healthcare systems creates an ecosystem that enables continuous monitoring, future-state analysis, and instant decision-making. This kind of cooperation is expected to yield good results in the case of neurodegenerative and infectious diseases. However, implementing QD in medical diagnostics faces several significant obstacles, including high growth costs, scalability issues, technical immaturity, and regulatory gaps. Nevertheless, further work is going on despite these limitations. The current chapter will focus on the technical basis, present uses, limitations, and future of quantum technology in diagnosis.
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.
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
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].
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.
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.
Quantum computing holds immense potential to revolutionize healthcare by enhancing diagnostic accuracy, Enhancing the effectiveness of medical treatments and optimizing healthcare data management through its distinctive computational capabilities enable the processing of complex datasets far beyond the reach of classical computers, offering groundbreaking advancements in various medical fields. This research topic explores the application of quantum computing in various aspects of healthcare, emphasizing how technology may improve data processing, diagnosis, and treatment planning. Quantum algorithms can accelerate the analysis of genomic data, improve medical imaging techniques, and optimize treatment strategies, paving the way for personalized and more effective healthcare interventions. Despite the bright future, there are still technological, moral, and legal issues that need to be resolved before quantum computing may be used in healthcare. The stability and scalability of quantum hardware, along with concerns about data security and patient privacy, present significant hurdles to widespread adoption. To fully realize the revolutionary potential of quantum computing in healthcare, these issues must be resolved via further study and teamwork.