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.
Abstract Quantum computing hardware and software have made enormous strides over the last years1. Questions around quantum computing’s impact on research and society have changed from “if” to “when/how”. The 2020s have been described as the “quantum decade”, and the first production solutions that drive scientific and business value are expected to become available over the next years. Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although medicine and quantum theory have arguably been entangled ever since Schrödinger’s cat2). The initial focus was on biochemical and computational biology problems3,4,5,6,7,8; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies from the last few years. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms, for instance, quantum support vector classifiers and quantum neural networks, have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and the applied algorithms are summarized. In addition, this review provides an outlook on medicine in the quantum era. There has been much discussion about healthcare’s journey towards precision medicine and the quadruple aim (better health, lower costs, enhanced patient experiences, and improved healthcare practitioner work lives)9. While a range of technical and ethical challenges remain, quantum computing is poised to become a key enabler for advancing towards the holy grail: keeping people healthy through proactive medical care and guidance at the level of an individual.
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.