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.
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.
Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.
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.
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.
Brain tumors are one of the most serious neurological diseases in which timely and accurate diagnosis contributes significantly to successful treatment. Magnetic Resonance Imaging (MRI) is extensively used to assess brain tumors, but manual interpretation is time consuming and clinician dependent. While deep learning techniques have made significant strides in automated tumor detection and classification, there are still challenges to address concerning interpretability, computational complexity, and trust in clinical applications. In this chapter, an explainable hybrid quantum-classical framework is introduced for brain tumor diagnosis in smart healthcare systems. The proposed framework includes deep learning models and variational quantum circuits for advancing diagnostic capabilities and enhances the transparency and clinical interpretability of these models through the integration of explainable artificial intelligence (XAI) methods, including Grad-CAM, SHAP, and LIME. This integration can enable intelligent clinical decision support systems in the field of neuro-oncology.
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.
The rapid advancement of Artificial Intelligence (AI) has heightened concerns about the opacity of decision-making processes in complex models, particularly deep neural networks. This research explores a novel paradigm that integrates quantum computing principles into Explainable AI (XAI) to enhance interpretability without compromising predictive accuracy. The study introduces a Quantum-inspired Interpretable Model (QIIM) framework that leverages Hilbert space embeddings, unitary transformations, and operator-based learning to represent knowledge in a mathematically transparent form. The proposed model computes interpretability through the expectation values of observable operators, enabling the decomposition of decisions into quantifiable, human-understandable components. To capture hierarchical relationships, a Tensor Network Interpretable Model (TNIM) is further developed, offering scalable insights into complex dependencies among features. Experimental evaluations—performed on benchmark datasets for decision-making tasks—demonstrate that the quantum-inspired models achieve competitive accuracy while significantly improving local and global interpretability metrics compared to classical XAI techniques. The findings underscore the potential of quantum formalism as a new foundation for transparent AI systems, bridging the gap between computational efficiency and explainability. This study contributes to both theoretical understanding and practical advancement in interpretable machine learning, paving the way for ethically aligned, transparent, and human-trustworthy AI-driven decision support systems.