Healthcare decision support demands models that are accurate on heterogeneous clinical data, fast enough for the point of care, and transparent enough to earn clinician trust. Classical learners meet parts of this brief but struggle to capture the high-order correlations that bind physiological variables together, and they typically expose little of their internal reasoning. This paper introduces QIDSS, a quantum-inspired machine-learning framework that runs entirely on commodity classical hardware yet borrows the mathematical machinery of quantum computation—amplitude encoding, parameterised unitary evolution, and entanglement-style feature interaction—to enrich the representation of clinical evidence. A patient record is mapped into a normalised state vector, a shallow variational circuit emulated through tensor operations transforms that state, and a Born-rule measurement yields a calibrated risk estimate together with a per-feature attribution. Training proceeds by a parameter-shift gradient over the emulated circuit, combined with cross-entropy and an entanglement-regularisation term that discourages spurious correlations. We evaluate the framework on a consolidated clinical dataset and benchmark it against logistic regression, gradient-boosted trees, a deep neural network, and a simulator-based variational quantum classifier. QIDSS reaches 94.5% accuracy with matching gains in precision and recall while holding average decision latency to 9.2 ms, comfortably below the quantum baseline. Beyond the headline numbers, the framework surfaces the features that most influenced each verdict and routes low-confidence cases to clinician review. Implementation overhead, qubit-count scaling, and deployment constraints are examined candidly.
Healthcare decision support demands models that are accurate on heterogeneous clinical data, fast enough for the point of care, and transparent enough to earn clinician trust. Classical learners meet parts of this brief but struggle to capture the high-order correlations that bind physiological variables together, and they typically expose little of their internal reasoning. This paper introduces QIDSS, a quantum-inspired machine-learning framework that runs entirely on commodity classical hardware yet borrows the mathematical machinery of quantum computation—amplitude encoding, parameterised unitary evolution, and entanglement-style feature interaction—to enrich the representation of clinical evidence. A patient record is mapped into a normalised state vector, a shallow variational circuit emulated through tensor operations transforms that state, and a Born-rule measurement yields a calibrated risk estimate together with a per-feature attribution. Training proceeds by a parameter-shift gradient over the emulated circuit, combined with cross-entropy and an entanglement-regularisation term that discourages spurious correlations. We evaluate the framework on a consolidated clinical dataset and benchmark it against logistic regression, gradient-boosted trees, a deep neural network, and a simulator-based variational quantum classifier. QIDSS reaches 94.5% accuracy with matching gains in precision and recall while holding average decision latency to 9.2 ms, comfortably below the quantum baseline. Beyond the headline numbers, the framework surfaces the features that most influenced each verdict and routes low-confidence cases to clinician review. Implementation overhead, qubit-count scaling, and deployment constraints are examined candidly.
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.
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.
Quantum machine learning (QML) has emerged as a transformative paradigm that leverages quantum mechanical phenomena superposition, entanglement, and quantum interference to enhance computational approaches in healthcare. This comprehensive survey systematically examines advances in QML for healthcare applications from 2020 to 2025, analyzing cutting-edge algorithms, recent implementations, and emerging prospects across drug discovery, medical imaging, genomics, and clinical decision support. We review Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and hybrid quantum-classical frameworks. Our analysis of 150+ recent publications reveals significant theoretical progress alongside persistent hardware constraints and algorithmic challenges. We provide a comprehensive taxonomy of QML algorithms, a comparative performance analysis against classical baselines, a detailed examination of NISQ-era limitations, and a technology roadmap spanning near-term hybrid approaches through long-term fault-tolerant quantum computing. This survey serves as an updated reference for researchers and practitioners navigating the rapidly evolving intersection of quantum computing and medical informatics.
The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.
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.
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.