BACKGROUND AND OBJECTIVE: Quantum machine learning is emerging as a promising extension of artificial intelligence, with potential advantages over classical approaches in handling complex biomedical data. This review aims to evaluate quantum machine learning applications in the detection, prediction, and personalized management of metabolic syndrome and type 2 diabetes mellitus. METHODS: We reviewed literature published between 1994 and 6 July 2026, with peer-reviewed journal articles and conference proceedings as the principal evidence base, complemented by selected preprints and technical sources. Quantum machine learning approaches, including quantum support vector machines, quantum neural networks, and quantum echo state networks, were classified and compared with classical counterparts across obesity and early metabolic dysregulation, diabetes diagnosis and glycemic management, and chronic complications. RESULTS: Quantum machine learning and hybrid quantum-classical systems demonstrated potential benefits in small-sample and noisy environments typical of wearable and biomedical sensor data. Reported performance gains included improvements in accuracy, robustness, and scalability, though interpretability and reproducibility remain challenges. Hardware limitations associated with noisy intermediate-scale quantum devices, data encoding, and privacy considerations emerged as key barriers to clinical translation. CONCLUSIONS: Preliminary studies highlight the promise of quantum machine learning for predictive and personalized management of metabolic syndrome and type 2 diabetes mellitus. However, successful clinical adoption will require robust validation pipelines, regulatory sandboxes, and harmonized compliance frameworks. Domain-specific evaluation metrics and transparent conformity assessments are essential to ensure trustworthy, scalable, and equitable deployment. A staged roadmap is proposed to bridge experimental progress with ethical and regulatory readiness.
BACKGROUND AND OBJECTIVE: Quantum machine learning is emerging as a promising extension of artificial intelligence, with potential advantages over classical approaches in handling complex biomedical data. This review aims to evaluate quantum machine learning applications in the detection, prediction, and personalized management of metabolic syndrome and type 2 diabetes mellitus. METHODS: We reviewed literature published between 1994 and 6 July 2026, with peer-reviewed journal articles and conference proceedings as the principal evidence base, complemented by selected preprints and technical sources. Quantum machine learning approaches, including quantum support vector machines, quantum neural networks, and quantum echo state networks, were classified and compared with classical counterparts across obesity and early metabolic dysregulation, diabetes diagnosis and glycemic management, and chronic complications. RESULTS: Quantum machine learning and hybrid quantum-classical systems demonstrated potential benefits in small-sample and noisy environments typical of wearable and biomedical sensor data. Reported performance gains included improvements in accuracy, robustness, and scalability, though interpretability and reproducibility remain challenges. Hardware limitations associated with noisy intermediate-scale quantum devices, data encoding, and privacy considerations emerged as key barriers to clinical translation. CONCLUSIONS: Preliminary studies highlight the promise of quantum machine learning for predictive and personalized management of metabolic syndrome and type 2 diabetes mellitus. However, successful clinical adoption will require robust validation pipelines, regulatory sandboxes, and harmonized compliance frameworks. Domain-specific evaluation metrics and transparent conformity assessments are essential to ensure trustworthy, scalable, and equitable deployment. A staged roadmap is proposed to bridge experimental progress with ethical and regulatory readiness.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary paradigm that integrates quantum computing and machine learning to address limitations of classical approaches in processing complex high-dimensional heterogeneous and imbalanced medical data. This study presents a systematic literature review of QML applications in healthcare based on 94 primary studies published between 2020 and April 2025. The review follows Kitchenham’s guidelines and the PRISMA framework to ensure rigor transparency and reproducibility. Seven research questions were formulated to examine publication trends sources datasets topics quantum model categories algorithms methods and evaluation metrics. Findings reveal a sharp increase in publication activity after 2023 indicating growing academic interest in healthcare-oriented QML. Medical imaging datasets were most frequently used followed by signal-based and tabular datasets with 86.5% of studies relying on public data. Hybrid classical-quantum approaches including CNN-QSVM Quantum Neural Networks and Variational Quantum Classifiers were dominant because they combine classical feature extraction with quantum-enhanced learning. However most studies were conducted in simulation environments rather than real hardware reflecting NISQ limitations including noise decoherence limited qubits and circuit depth constraints. Many results used small curated or imbalanced datasets raising concerns regarding generalizability reproducibility and clinical validity. Although numerous studies reported high accuracy standardized evaluation protocols external validation and uncertainty assessment remain limited. Overall QML in healthcare remains exploratory with substantial potential but significant barriers. This review highlights progress gaps and future directions emphasizing clinically representative datasets robust benchmarking interpretable noise-resilient models and real-hardware implementation to support trustworthy deployment in healthcare systems worldwide settings today.
Blood glucose is one of the key indicators influencing the body’s metabolic performance. Accurate prediction and proactive management of this parameter are of paramount clinical importance. In this context, the analysis and evaluation of quantum machine learning (QML) algorithms for processing medical data and enhancing the predictive accuracy of health-related models have emerged as cutting-edge research directions in recent studies. Leveraging the principles of quantum physics and computation, QML algorithms serve as a promising alternative to classical methods for predicting chronic diseases. By mapping classical data into a quantum feature space and exploiting intrinsic quantum properties such as superposition and entanglement, these algorithms significantly enhance the ability to detect complex and latent patterns within datasets. In this study, the dataset was first preprocessed using Principal Component Analysis (PCA) for dimensionality reduction and noise suppression. Subsequently, the predictive performance of two quantum machine learning models—the Quantum Support Vector Machine (QSVM) and the Variational Quantum Circuit (VQC)—was evaluated and compared with that of the classical Support Vector Machine (SVM) for blood glucose level prediction. The results indicate that the QSVM model achieves a prediction accuracy of 90%, outperforming the classical SVM, which attained an accuracy of 74.4%. Moreover, the hybrid classical–quantum VQC model demonstrated the highest predictive performance, achieving an accuracy of 94.2%, thereby establishing it as the most effective approach for blood glucose level forecasting in this experimental setting.
Background Artificial Intelligence (AI) incorporation within healthcare applications has significantly helped in the improvement of healthcare diagnostics, patient forecasting, and clinical decision-making. The steady growing of medical data in terms of quantity, quality, and dimension poses more significant problems for traditional AI methods in terms of size, time, and computational efficiency. However, classical machine learning models often struggle with high-dimensional, imbalanced, and heterogeneous medical datasets, which limits their scalability and generalizability. The Quantum Artificial Intelligence (QAI) model, which combines quantum computing and machine learning in a complementary way, has emerged as one of the main trends to defeat these problems. This complementary approach is gaining more popularity, with the most advanced work being developed in this area, as it offers a fundamental explanation of current complexities and the underlying principles and their applications. Methodology This paper begins with the basic concepts of quantum computing and where it overlaps with AI. It then moves to a comparative approach, discussing various quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and variational quantum circuits. A comparative evaluation of quantum support vector machine, variational quantum classifier, quantum neural network, quantum K-means, and quantum Boltzmann machine is conducted using the Pima Indians Diabetes dataset to assess model behaviour and performance under standard metrics. Further detail is provided on the theoretical and practical implications of QAI in medical imaging, genomics, electronic health records, and drug discovery. Additionally, this paper examines platforms, tools, and quantum hardware used for QAI research, and discusses unique challenges currently faced in quantum technology. Results Unlike prior surveys that primarily provide conceptual overviews, this paper uniquely combines a comprehensive review with practical benchmarking of multiple quantum machine learning models on a benchmark medical dataset. This provides comparative insights into algorithmic performance under real-world constraints, particularly in relation to high-dimensional medical data, genomic data, electronic health records with large number of clinical attributes, radiology images, etc. In this study, dimensionality arises from multivariate clinical features encoded into quantum feature maps and the limitations of current quantum systems. Conclusion This paper aims to serve as a starting point for researchers and practitioners interested in the potential of quantum-enhanced AI to transform health data analytics. By identifying current limitations, evaluating the performance of emerging quantum machine learning approaches, and outlining gaps in the literature, the paper highlights future research directions necessary to advance QAI in healthcare. Overall, the findings outline both the potential and practical constraints of quantum models, offering a foundation for future work toward scalable, clinically applicable QAI solutions.
Prompt diagnosis of disease is a key determinant in reducing mortality, improving patient care and decreasing healthcare costs. Despite the success of classical machine learning (ML) in improving predictive modeling across oncology, cardiology, and neurology, growing data dimensionality and computational complexity remain ongoing barriers. Quantum computing based on superposition and entanglement is a promising computational paradigm that shows potential advantages in high-dimensional pattern recognition and kernel-based learning. This review aims to systematically summarize preliminary work on quantum machine learning (QML) with empirical applications for early disease diagnosis, focusing on its theoretical underpinnings in addition to its algorithmic design and use in healthcare. We then frame the discussion with respect to quantum support vector machines and variational quantum classifiers, quantum kernels, as well as hybrid quantum–classical architectures. We further evaluate existing hardware constraints in the noisy intermediate-scale quantum (NISQ) era and appraise translational maturity for integration into public health. While clinical deployment is still far from broad, hybrid QMLs are proving useful in low-data and complex-feature contexts. We end with research challenges, regulatory guidance, and strategic directions towards enabling quantum-enhanced precision diagnostics aligned with U.S. public health innovation initiatives.
Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation.
Healthcare 5.0 combines artificial intelligence, the Internet of Medical Things, edge and cloud computing, and digital twins to support safer, more personal, and more efficient care. Interest in quantum machine learning is growing in this space, but the current evidence is fragmented and hard to compare across studies. Many reports focus on small tasks, use weak or inconsistent baselines, and do not separate algorithmic gains from costs caused by data encoding, repeated circuit execution, and hardware noise. This survey addresses these gaps by providing a structured synthesis of Healthcare 5.0 applications and by offering a practical comparison between classical machine learning and quantum machine learning using criteria that matter for clinical use. We review representative use cases in medical imaging, electronic health record based prediction, remote monitoring, decision support, and digital twin enabled systems. We then analyze QML integration patterns that are realistic today, with emphasis on hybrid pipelines where classical models extract features and quantum components act as narrow decision or optimization modules. Finally, we consolidate the barriers that currently limit adoption, including encoding overhead, limited qubit counts and circuit depth, training instability and reproducibility, privacy and governance constraints, and the need for careful benchmarking and reporting. We conclude with guidance on evaluation and study design to support fair comparison, clearer clinical relevance, and better alignment with Healthcare 5.0 engineering requirements.