As the field of medicine grows and expands, new scientific developments hold great promise for improving quality of care, clinical research, and the diagnosis and treatment of diseases. Quantum physics is a promising field that intersects with medicine much more than originally understood. In terms of diagnosing different diseases, incorporating quantum mechanics into the study of medicine can allow for efficient diagnosis before symptoms even arise in a patient. Applying theory-based mathematical structures that describe neuron transmission throughout the brain and mind on a quantum scale can help us to better understand neurological diseases in patients. Quantum theory can even give plausible explanations for subtle DNA changes and even telomere reduction in patients who develop cancer. Utilizing quantum theory in the field of medicine can help in understanding and applying treatments for a multitude of different diseases, such as Alzheimer's disease or diverse types of cancer, and even expand upon efficient and reliable diagnosis in clinical settings. Quantum physics is a pertinent advancing field that may have a significant impact on expanding medical care and treatment in the near future. In this review, the application of quantum physics in medicine is discussed.
As the field of medicine grows and expands, new scientific developments hold great promise for improving quality of care, clinical research, and the diagnosis and treatment of diseases. Quantum physics is a promising field that intersects with medicine much more than originally understood. In terms of diagnosing different diseases, incorporating quantum mechanics into the study of medicine can allow for efficient diagnosis before symptoms even arise in a patient. Applying theory-based mathematical structures that describe neuron transmission throughout the brain and mind on a quantum scale can help us to better understand neurological diseases in patients. Quantum theory can even give plausible explanations for subtle DNA changes and even telomere reduction in patients who develop cancer. Utilizing quantum theory in the field of medicine can help in understanding and applying treatments for a multitude of different diseases, such as Alzheimer's disease or diverse types of cancer, and even expand upon efficient and reliable diagnosis in clinical settings. Quantum physics is a pertinent advancing field that may have a significant impact on expanding medical care and treatment in the near future. In this review, the application of quantum physics in medicine is discussed.
As the field of medicine grows and expands, new scientific developments hold great promise for improving quality of care, clinical research, and the diagnosis and treatment of diseases. Quantum physics is a promising field that intersects with medicine much more than originally understood. In terms of diagnosing different diseases, incorporating quantum mechanics into the study of medicine can allow for efficient diagnosis before symptoms even arise in a patient. Applying theory-based mathematical structures that describe neuron transmission throughout the brain and mind on a quantum scale can help us to better understand neurological diseases in patients. Quantum theory can even give plausible explanations for subtle DNA changes and even telomere reduction in patients who develop cancer. Utilizing quantum theory in the field of medicine can help in understanding and applying treatments for a multitude of different diseases, such as Alzheimer's disease or diverse types of cancer, and even expand upon efficient and reliable diagnosis in clinical settings. Quantum physics is a pertinent advancing field that may have a significant impact on expanding medical care and treatment in the near future. In this review, the application of quantum physics in medicine is discussed.
Quantum computing is quickly revolutionizing medicine, promising unprecedented levels of processing, analysis, and decision-making abilities. This review discusses recent developments in applying quantum computing to medical imaging, diagnostics, and treatment planning. Quantum machine learning and hybrid quantum-classical models have been used by researchers to show improved accuracy, speed, and scalability in medical image classification, segmentation, and disease prediction. Quantum-improved algorithms are being implemented for early detection of multifactorial diseases like cancer and neurologic disorders with potential for advancing clinical prognoses and biomarker discovery. Moreover, quantum cryptography methods are under development to enhance security for private medical information with concerns regarding expanding privacy and security risks. Improvements in quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) also hold promise in enhancing diagnostic efficiency and resource savings. This review synthesizes existing applications, identifies new trends, and delineates challenges and future directions of quantum computing in medicine. It underscores the importance of explainable and interpretable quantum AI systems to maintain clinical trust and utility. The results indicate that quantum technologies, although in their infancy, have transformative potential for precision medicine, personalized therapy, and sophisticated medical decision-making.
This research explores how quantum physics is redefining the landscape of medical diagnostics through sensing, imaging, and computation. It examines the principles of quantum mechanics superposition, entanglement, and coherence and their application in developing technologies capable of detecting disease at molecular and cellular levels. Quantum imaging surpasses classical resolution limits, while quantum machine learning enables faster and more accurate analysis of medical data. The study also evaluates real-world clinical applications, including wearable magnetoencephalography, quantum-enhanced MRI, and biosensing for cancer biomarkers. Alongside these advancements, it addresses ethical, economic, and regulatory challenges such as data security, cost, and accessibility. The findings suggest that quantum technologies hold transformative potential to shift medicine from reactive treatment to predictive, preventive care provided innovation remains grounded in transparency, inclusivity, and interdisciplinary collaboration.
Quantum computing is fast transforming the medical world and giving it a new level of speed, accuracy, and safety in the health-care information. The latest developments use quantum machine learning and quantum-classical to improve medical imaging like classification, segmentation, and disease prediction, going beyond the capability of the classical algorithms. In the multifactorial disease cases, like cancer and neurological diseases, quantum-enhanced analytics will yield better clinical prognoses, better biomarker discoveries, and earlier detection. In the meantime, quantum-cryptography protocols are under development that will provide protection against privacy threats that are constantly changing to harm patient data. State-of-the-art quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) continue to optimize the process of diagnostics, reducing resource consumption, without losing the level of accuracy. This review maps these new applications, identifies the new trends, and talks about the future challenges of such applications, especially the requirement of explainable, interpretable quantum AI, which the clinicians can believe in. In brief, quantum technologies are in their infancy, but will have the ability to advance precision medicine, customised treatment and revolutionise medical decision-making.
Over the past few decades, medical imaging has revolutionized the way clinicians diagnose, monitor, and treat diseases. However, despite significant advancements in imaging resolution, contrast, and computational reconstruction, several challenges remain unresolved — especially in terms of early-stage detection, noise reduction, and functional imaging at the cellular or molecular level. In recent years, quantum physics has emerged as a disruptive force with the potential to drastically enhance medical imaging technologies through the application of its fundamental principles such as quantum superposition, entanglement, tunneling, and spin dynamics. This research explores the intersection of quantum mechanics and medical imaging engineering, with a primary focus on how quantum phenomena are being utilized to elevate the performance and capability of key imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and optical-based quantum imaging. The study presents an in-depth analysis of how quantum concepts are applied in practice — such as the role of spin alignment and magnetic resonance in MRI, the use of entangled photon pairs in PET scanning, and the integration of quantum dots in high-contrast optical imaging. Beyond existing technologies, the paper investigates cutting-edge developments including quantum-enhanced sensors, quantum computing for real-time image reconstruction, and the promise of quantum artificial intelligence in image interpretation. By analyzing both theoretical frameworks and experimental findings, the study highlights the significant advantages that quantum technologies bring — such as improved spatial resolution, higher signal-to-noise ratio, and reduced patient exposure to radiation. Despite its promise, the integration of quantum physics into medical imaging is not without challenges. The research discusses limitations such as system decoherence, scalability, clinical costs, and the gap between laboratory research and clinical deployment. In conclusion, this paper argues that while still emerging, the application of quantum physics holds transformative potential for the future of diagnostic imaging, and could ultimately lead to more accurate, efficient, and personalized healthcare.
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