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.
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.
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.
Quantum computing is a developing computational paradigm that uses the principles of quantum mechanics, like superposition and entanglement, to execute composite calculations beyond the abilities of conventional computers. Currently, implementation of this technology in medicine has the potential to revolutionize various domains like drug discovery, protein folding, genomics, medical imaging, and personalized healthcare. By allowing high-dimensional data processing and accurate molecular simulations, quantum computing may enhance biomedical research and thereby improving clinical decision-making. Moreover integrating quantum computing with artificial intelligence, mainly through quantum machine learning, further accelerates its capability to assess vast complex biological datasets and generate precise predictive models. Applications like disease prediction, drug response optimization, and advanced diagnostic imaging further validates its growing importance in modern healthcare. However, the implementation of quantum computing in medicine is presently limited by technical challenges, that includes decoherence, error correction, and scalability, as well as practical problems like high budgets and lack of skilled professionals. Moreover, ethical concerns play a major hurdle related to data privacy and healthcare inequality also require cautious consideration. In spite of these challenges, ongoing developments in hybrid quantum-classical systems and quantum algorithms reveal that a promising future, particularly placing quantum computing as an effective transformative force in next-generation medicine.
Abstract Quantum computing (QC) has emerged as a transformative technology with the potential to surpass classical computational limits, especially in complex domains like medical imaging. This study investigates the integration of QC and quantum machine learning (QML) into medical imaging, with a focus on MRI, EEG, and CT. A structured literature review was conducted to identify recent developments, focusing on studies using accessible open-source data and English-language peer-reviewed publications. By leveraging quantum principles such as superposition and entanglement, hybrid quantum-classical models have demonstrated enhanced accuracy, speed, and efficiency in diagnostic tasks. In MRI, QML has improved early detection and classification of neurological diseases like Alzheimer’s and brain tumours. In EEG analysis, quantum algorithms such as Quantum EEGNet and Quantum Support Vector Machines have shown superior performance in detecting disorders, including schizophrenia and autism spectrum disorder. Additionally, quantum algorithms for CT image reconstruction and classification have achieved faster processing with fewer artifacts and greater fidelity. Despite current hardware constraints, results highlight the promise of QC in addressing existing limitations in medical diagnostics. The findings support continued development of QML models and quantum-enhanced workflows, with potential to revolutionise clinical practice by offering more accurate, efficient, and personalised care in neurological imaging and analysis.
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 has emerged as a disruptive paradigm which can transform the information heavy areas of knowledge such as precision medicine. The expanding needs of precision medicine based on customized therapy in the light of genomics, molecular profiling, and clinical data suggest that the computation tasks can demand computational capabilities unattainable by the traditional high-performance computing. Quantum algorithms, which are founded upon the laws of superposition, entanglement and quantum parallelism, offer a scale factor of exponential on a problem in genomics, drug discovery, medical imaging and biomarker discovery. This is a review article that is a narrative synthesis of emerging trends at the interface of quantum computing and precision medicine. In particular, it discusses women topics such as patient stratification and imaging analysis with quantum machine learning (QML), protein-ligand interactions and drug discovery with quantum chemistry simulations, and quantum-classical architectures to combine complex biomedical data with clinical decision-making. Initial implementations show evidence of proof-of-concept utility in accelerating the sequencing of genomes, improving the accuracy of diagnostic imaging, and improving treatment design. With those developments, there are still major issues, such as the problem of qubit coherence, scalability of algorithms, barriers to the integration of data, and ethical concerns about transparency and fair access. To overcome these obstacles, it is necessary to physically co-evolve domain-specific quantum algorithms, powerful error-correction protocols, and cloud-friendly frameworks that can connect biomedical research to current quantum computing devices. This review offers a roadmap on how quantum computing can be leveraged to drive precision medicine to scalable, interpretable, and patient-centered healthcare solutions by outlining both early uses and future opportunities.
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.