Introduction: This review examines quantum computing (QC) applications in clinical care, emphasizing advancements directly impacting patient outcomes. QC holds transformative potential in medicine, particularly through enhancing diagnostic accuracy, optimizing treatment plans, and enabling real-time decision-making. Methods: A systematic analysis of 35 studies published between 2015 and 2024 was conducted. The studies were evaluated for their contributions to diagnostic, therapeutic, and decision-support improvements in clinical care enabled by quantum computing technologies. Results: The analysis revealed QC's promise in improving diagnostic accuracy in medical imaging, optimizing treatments in oncology, and enhancing real-time clinical decision-making. QC-driven algorithms demonstrated potential to enhance diagnostic accuracy and computational efficiency. These improvements could enable earlier detection of diseases such as Alzheimer's, cancer, and osteoarthritis, supporting more timely interventions and better prognoses. Discussion: Despite promising outcomes, current limitations-such as hardware scalability, error mitigation, and ethical considerations-hinder widespread adoption of QC in clinical settings. Overcoming these challenges will require interdisciplinary collaboration and technological innovation. The review underscores QC's capacity to deliver precise, personalized, and efficient care, advocating for its integration into healthcare workflows to advance precision medicine and improve patient outcomes.
Introduction: This review examines quantum computing (QC) applications in clinical care, emphasizing advancements directly impacting patient outcomes. QC holds transformative potential in medicine, particularly through enhancing diagnostic accuracy, optimizing treatment plans, and enabling real-time decision-making. Methods: A systematic analysis of 35 studies published between 2015 and 2024 was conducted. The studies were evaluated for their contributions to diagnostic, therapeutic, and decision-support improvements in clinical care enabled by quantum computing technologies. Results: The analysis revealed QC's promise in improving diagnostic accuracy in medical imaging, optimizing treatments in oncology, and enhancing real-time clinical decision-making. QC-driven algorithms demonstrated potential to enhance diagnostic accuracy and computational efficiency. These improvements could enable earlier detection of diseases such as Alzheimer's, cancer, and osteoarthritis, supporting more timely interventions and better prognoses. Discussion: Despite promising outcomes, current limitations-such as hardware scalability, error mitigation, and ethical considerations-hinder widespread adoption of QC in clinical settings. Overcoming these challenges will require interdisciplinary collaboration and technological innovation. The review underscores QC's capacity to deliver precise, personalized, and efficient care, advocating for its integration into healthcare workflows to advance precision medicine and improve patient outcomes.
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.
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.
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 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.
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in 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.