Pediatric urological disorders require timely recognition to reduce the risk of persistent functional, reproductive, and psychosocial consequences, yet screening still depends heavily on clinician-led visual examination. This study develops and evaluates Quantum Vision AI, a child-safe intelligent screening platform that integrates non-invasive medical imaging, Hybrid Quantum Machine Learning, explainable artificial intelligence, and clinical decision support. A Design Science Research approach combined with experimental quantitative validation was used to construct the prototype. Standardized pediatric external-genital images were preprocessed and analyzed through a hybrid quantum-classical architecture in which classical vision models extracted anatomical features, quantum circuits enhanced feature representation, and explainability methods visualized diagnostically relevant regions. The proposed platform consistently outperformed the conventional convolutional, EfficientNetV2, and Vision Transformer baselines used in the study, while its attention maps concentrated on clinically meaningful anatomical structures and expert reviewers reported favorable usability and diagnostic relevance. These findings demonstrate the feasibility of quantum-enhanced computer vision for pediatric urological screening and support further multicenter validation, dataset expansion, and clinical translation. The platform is intended to augment, rather than replace, physician judgment in early screening and referral decisions.
Pediatric urological disorders require timely recognition to reduce the risk of persistent functional, reproductive, and psychosocial consequences, yet screening still depends heavily on clinician-led visual examination. This study develops and evaluates Quantum Vision AI, a child-safe intelligent screening platform that integrates non-invasive medical imaging, Hybrid Quantum Machine Learning, explainable artificial intelligence, and clinical decision support. A Design Science Research approach combined with experimental quantitative validation was used to construct the prototype. Standardized pediatric external-genital images were preprocessed and analyzed through a hybrid quantum-classical architecture in which classical vision models extracted anatomical features, quantum circuits enhanced feature representation, and explainability methods visualized diagnostically relevant regions. The proposed platform consistently outperformed the conventional convolutional, EfficientNetV2, and Vision Transformer baselines used in the study, while its attention maps concentrated on clinically meaningful anatomical structures and expert reviewers reported favorable usability and diagnostic relevance. These findings demonstrate the feasibility of quantum-enhanced computer vision for pediatric urological screening and support further multicenter validation, dataset expansion, and clinical translation. The platform is intended to augment, rather than replace, physician judgment in early screening and referral decisions.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Abstract The convergence of quantum computing and medical image analysis has emerged as a promising frontier for advancing diagnostic intelligence beyond the limits of classical machine learning. Recent developments in quantum hardware and quantum machine learning (QML) algorithms have stimulated growing interest in their potential to enhance complex medical imaging tasks, including disease classification, image segmentation, and clinical decision support (Wei et al., 2023; Ullah and Garcia-Zapirain, 2024). This study presents a comprehensive review and synthesis of state-of-the-art QML methodologies applied to medical imaging, systematically examining both theoretical foundations and empirical implementations. We analyze quantum-based and hybrid quantum–classical models employed across modalities such as MRI, CT, X-ray, and histopathological imaging, with particular attention to classification accuracy, computational efficiency, and diagnostic robustness (Maheshwari et al., 2022; ElBedoui et al., 2025). Our findings reveal clear trends indicating that hybrid quantum–classical approaches currently dominate practical applications, consistently outperforming purely classical baselines in select low-dimensional and domain-specific diagnostic tasks. Performance gains are most evident in feature-rich yet data-constrained scenarios, where quantum kernels and variational circuits demonstrate enhanced representational capacity (Ajlouni et al., 2023; Landman et al., 2022). However, these advantages remain context-dependent and sensitive to noise, circuit depth, and encoding strategies. Overall, QML holds significant promise for improving medical image-based diagnosis, but its widespread clinical adoption is constrained by hardware scalability, limited qubit coherence, and unresolved data encoding challenges. Addressing these limitations will be critical for translating QML from experimental feasibility to routine medical practice (Marengo and Santamato, 2025; Rawas and AlSaeed, 2026).
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-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.
Renal Cell Carcinoma (RCC) is a leading tumour of the kidney and among most common cancer in adults. 30-40% is often diagnosed when already (in) advanced stage, since there is no specific symptoms, but earliest for these patients infancy detection of RCC. This case emphasizes the importance of precise diagnosis from cellular to tubular-epithelial-lining kidney level that results in successful management and good prognosis. Cryofocus and quantum therapy on the background of modern achievements of artificial intelligence and quantum computers in medical attention Recent achievements of Artificial Intelligence (AI) and quantum computer technologies, and medical visualization are solving some problems experienced by the classic methods of treatment long ago. In this paper, we propose a novel quantum networks model (QNM) by AI which joins the quantum enhanced neural computation with high optical resolution medical imaging data for diagnosis of RCC. Here we have shown a quantum version of an innovation for image recognition in which the pixels are not the fundamental unit of the model, but qubits are. G can now learn to represent finer textures, e.g., complex visual patterns that description which is not directly described by CNNs. Pre-processed images from classical computing (histopathological slides, contrast enhanced CT scans) were input to a quantum-classically hybrid system. This architecture features encoding with quantum circuits and variational quantum layers, which learn complex relationships between RCC types of features and epithelial tissue morphology. Our experiments also demonstrate for the first time, the superiority of our hybrid quantum neural model over the classical deep learning methods in various computational sub-tasks of renal tissue anomaly diagnosis — segmentation, classification and grading. Comprehensive validation on heavily curated large-scale datasets consistently shows significantly better accuracy, sensitivity and specificity, which can be in orders of magnitudes, challenging classical models especially in regions where they diverge. Feedback from expert clinicians (oncologists and pathologists) indicates very good correlation of quantum-based features to pathological RCC biomarkers. Their work is a step toward something that kills so many people each year, and something vastly more accurate and efficient to diagnose. Quantum AI, medical imaging and combination are part of an era-defining decision-supporting system. It might mean earlier detection, less false positives and better outcomes for RCC patients. This model is sector independent and can be generalised to other cancer types and imaging modalities as well.
Early and accurate recognition of cardiovascular disease (CVD) using medical imaging is essential for timely clinical intervention and management. The objective of this study was to continue developing a hybrid quantum-classical deep learning system for the early detection and automatic triage of cardiovascular diseases using cardiac imaging information. The proposed method consists of a combination of efficient feature extraction via a lightweight classical convolutional neural network and a quantum-illness feature transformation and decision-refinement process implemented as a variational quantum circuit. A clinically informed triage module was added to rate patients into three risk categories (low-, moderate-, and high-risk). Experimental evaluation on a public cardiac magnetic resonance imaging (MRI) dataset revealed that the proposed framework achieved 94.5% classification accuracy and an area under the curve (AUC) of 0.96, with sensitivity exceeding that of state-of-the-art classical deep learning models. The results show that quantum-enhanced feature representations provide better class separability, robustness to Imaging noise, and improved learning efficiency. The results suggest that one way to overcome the limitations of conventional deep models is the hybrid quantum-classical learning approach, which is also compatible with near-term quantum hardware. This work raises the question of whether quantum-assisted artificial intelligence can enable scalable, real-time cardiovascular decision support systems and promote next-generation intelligent healthcare systems.