Quantum computing, agentic artificial intelligence (AI), and machine learning (ML) are converging to reshape adaptive medical imaging. This paper presents a hybrid Agentic Quantum Machine Learning (AQML) framework featuring a Quantum Reasoner Agent (QRA), a specialized sub-agent that performs high-dimensional optimization, kernel reasoning, and uncertainty modelling using variational quantum circuits within an LLM-orchestrated agentic pipeline. To address the documented limitations of outdated baselines and insufficient benchmarking in prior quantum medical imaging work, the AQML framework is evaluated against four modern architectures on an identical 500-slice BraTS 2023 subset. AQML achieves Dice = 0.910 and AUC = 0.970, outperforming nnU-Net (Dice 0.890), SwinUNet (0.876), TransUNet (0.872), and UNet (0.860). Entropy-aware regularization reduced mean quantum entropy from 0.48 to 0.37, and uncertainty calibration improved by 12% over the classical UNet baseline. IBM Quantum hardware characterization (ibmq_toronto: mean T1 = 91.5 μs, mean CNOT error = 1.08%) provides a concrete noise baseline for near-term feasibility planning. The Entanglement-Aware Regularization (EAR) term and the LLM-governed agentic feedback loop are mathematically specified in full, with Algorithm 1 detailing the complete Adam-SPSA training protocol and three structured refinement actions. The explainability layer and multi-modal fusion across CT and PET modalities are architectural design features pending formal clinical validation, as explicitly acknowledged in the limitations. This framework lays the foundation for scalable, federated, and ethically grounded quantum-intelligent radiology systems.
Quantum computing, agentic artificial intelligence (AI), and machine learning (ML) are converging to reshape adaptive medical imaging. This paper presents a hybrid Agentic Quantum Machine Learning (AQML) framework featuring a Quantum Reasoner Agent (QRA), a specialized sub-agent that performs high-dimensional optimization, kernel reasoning, and uncertainty modelling using variational quantum circuits within an LLM-orchestrated agentic pipeline. To address the documented limitations of outdated baselines and insufficient benchmarking in prior quantum medical imaging work, the AQML framework is evaluated against four modern architectures on an identical 500-slice BraTS 2023 subset. AQML achieves Dice = 0.910 and AUC = 0.970, outperforming nnU-Net (Dice 0.890), SwinUNet (0.876), TransUNet (0.872), and UNet (0.860). Entropy-aware regularization reduced mean quantum entropy from 0.48 to 0.37, and uncertainty calibration improved by 12% over the classical UNet baseline. IBM Quantum hardware characterization (ibmq_toronto: mean T1 = 91.5 μs, mean CNOT error = 1.08%) provides a concrete noise baseline for near-term feasibility planning. The Entanglement-Aware Regularization (EAR) term and the LLM-governed agentic feedback loop are mathematically specified in full, with Algorithm 1 detailing the complete Adam-SPSA training protocol and three structured refinement actions. The explainability layer and multi-modal fusion across CT and PET modalities are architectural design features pending formal clinical validation, as explicitly acknowledged in the limitations. This framework lays the foundation for scalable, federated, and ethically grounded quantum-intelligent radiology systems.
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).
Multi-modal biomedical imaging has become central to modern diagnostics, offering complementary anatomical, functional, and molecular information through modalities such as MRI, CT, PET, ultrasound, and optical imaging. Yet integrating these heterogeneous datasets remains computationally demanding due to differences in spatial resolution, noise profiles, acquisition dynamics, and high-dimensional feature distributions. This paper investigates AI–quantum hybrid diagnostic systems as an emerging paradigm for multimodal image fusion, reconstruction, and disease classification. By combining deep learning architectures with quantum-enhanced algorithms—including variational quantum circuits, quantum feature encoders, and quantum kernel methods—the hybrid framework aims to accelerate image processing, improve cross-modal consistency, and enhance diagnostic precision. The analysis highlights advancements in quantum-accelerated denoising, multi-modal registration, probabilistic inference, and high-dimensional pattern recognition. It also evaluates workflow integration challenges, such as NISQ-era noise, hardware scalability, and clinical interpretability. Overall, AI–quantum hybrid systems represent a promising frontier in medical imaging, offering potential improvements in speed, sensitivity, and personalized diagnostic accuracy.
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.
The integration of Quantum Computing with Artificial Intelligence (AI) presents a transformative opportunity for advanced medical imaging analysis. This paper proposes an Explainable Quantum AI (XQAI) framework for radiology decision support systems, aimed at enhancing diagnostic accuracy while ensuring interpretability of results. The approach leverages quantum kernel-based feature extraction to capture complex spatial–spectral patterns in MRI, CT, and PET scans, enabling superior detection of subtle anomalies. A hybrid quantum- classical deep learning model is employed, where quantum circuits perform high-dimensional feature mapping, and classical neural networks execute classification tasks. To address the "black-box" challenge in AI-assisted diagnosis, the framework incorporates quantum explainability modules using measurement-driven saliency mapping and Shapley value–based feature attribution. This dual- layer explainability provides radiologists with both a quantum- informed feature importance ranking and a visual heatmap overlay on the medical image. Experimental evaluation on publicly available medical imaging datasets demonstrates improved detection sensitivity (7.3%) and diagnostic confidence compared to conventional deep learning approaches, while reducing model opacity. The proposed XQAI system offers a pathway towards trustworthy, high-performance, and clinically deployable quantum-assisted diagnostic tools in radiology.
Deep learning has transformed radiology, but the computational cost of training and deploying modern models is outpacing what classical hardware can support. Quantum computing has been proposed as one response to this bottleneck: an n-qubit system can represent 2n states simultaneously, although practical speedups require efficient algorithms, data encoding, and measurement strategies rather than superposition alone. This review introduces radiologists to five core quantum concepts, qubits, superposition, entanglement, quantum gates, and quantum circuits, and to the superconducting, trapped-ion, and photonic hardware platforms that define the current noisy intermediate-scale quantum (NISQ) era. We then summarize the quantum machine learning architectures most often applied to medical imaging (quantum support vector machines, quantum neural networks, variational quantum circuits, and quantum convolutional neural networks) and quantum image representations such as the flexible representation of quantum images (FRQI) and the novel enhanced quantum representation (NEQR). Reported applications include brain tumor classification and segmentation on MRI, pneumonia detection on chest radiographs, skin and breast lesion classification, CT and PET reconstruction, and ultrasound denoising. Nearly all published systems are hybrid quantum-classical pipelines evaluated on small, downsampled benchmark datasets or simulators, and clinical deployment is not yet feasible. We conclude with current limitations and translational priorities for radiology.
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.