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.
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.
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.
Hybrid quantum-classical intelligence is a new promising paradigm to develop personalized smart healthcare systems with exemplification of computational power of quantum computing and scalability of classical artificial intelligent. This chapter introduces a hybrid design based on quantum machine learning, healthcare analytics, explainable AI, federated learning, and intelligent clinical decision support to apply precision medicine via this design. The given architecture will allow analyzing multimodal healthcare data efficiently, predicting the disease-risk, optimizing treatment plans on a personal scale, interpreting medical images, and predictive healthcare analytics. The framework enables the next-generation smart healthcare ecosystems by improving the computational efficiency, decision accuracy, transparency, and preserving privacy to enhance the realization of intelligent, patient-centric healthcare services.
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.
According to the World Health Organization, cardiovascular diseases are responsible for several million deaths worldwide each year, thus highlighting a prime necessity for developing methods of diagnosis that are accurate and rapid. Although recent advancements in quantum-enhanced machine learning have demonstrated enhanced prediction performance, current research is still limited by the reliance on static datasets, the lack of interpretability, and the lack of privacy-preserving mechanisms for data sharing across institutions. In this paper, we introduce an Explainable Quantum-Enhanced Federated Learning Framework for Real-Time Cardiovascular Risk Assessment that integrates the scalability of federated learning with the computational benefits of quantum models to allow multiple healthcare centers and remote devices to train models collaboratively without sharing raw patient data. Quantum-inspired classifiers such as variational circuits and Quantum SVM are integrated into the federated pipeline to accelerate convergence and improve robustness. An explanation layer was implemented to enhance clinical trust by applying changed feature attribution methods that help the physicians comprehend how much the important factors like cholesterol, blood pressure, and ECG patterns contribute to the risk predictions. In addition, the system is meant for constant watch by the mixing of electronic health records and wearable sensor streams, thus making it possible to switch from one-time prediction to dynamically and personally tailored risk assessment. The proposed technique has been validated through experiments on standard heart disease datasets where it is also said to have done the opposite: it has improved predictive accuracy, reduced communication overhead compared to traditional federated systems, and provided insights that are ready for supporting clinical decision-making in real practice. Consequently, this framework is a first step towards quantum-driven healthcare solutions that are safe, scalable, and explainable.
The rapid pace of development in healthcare artificial intelligence requires architectures that transcend predictive analytics to better understand causality and generate simulations of patient trajectories. This study proposes the Quantum Edge Federated Graph Transformer (QFGT), a hybrid quantum-inspired approach aimed at empowering causal, generative, and privacy-preserving digital twin healthcare. The model incorporates quantum kernel attention for complex-amplitude feature embedding, graph transformer reasoning mechanism for relational inference over multimodal clinical entities, and federated optimization for secure multi-institutional learning. Quantum-inspired kernel mappings in Hilbert space encode entangled dependencies between laboratory, imaging, genomic, and clinical text data, while a causal regularization layer constrains learned representations to adhere to interpretable cause-and-effect relations learned from structural causal models. The generative digital-twin module uses a diffusion-based latent simulator that predicts personalized trajectories of the disease, and it supports what-if counterfactual interventions. Federated deployment at the edge healthcare nodes enables model training with data decentralization and strict compliance with HIPAA and GDPR privacy regulations. Experimental work on multimodal clinical data shows an accuracy of 98.1% with a mean early-detection window of 7.9 months and F1-scores >0.98 for all diseases, in addition to an increase in minority-cohort recall of 6.5% via equitable quantum-kernel feature sharing. The proposed QFGT framework opens up a new direction for the quantum-inspired, federated, and causally explainable digital twin systems, which lead to trustworthy, proactive, and personal healthcare intelligence at the quantum edge.
The growing amount of data obtained from electronic health records, medical imaging systems, wearable devices, genetic data banks and the Internet of Medical Things is making decision making in healthcare settings, medical analytics challenging and data safety and security difficult. A novel paradigm known as Quantum-Enhanced Intelligent Healthcare Framework is presented. It not only merges Artificial Intelligence and Quantum Machine Learning, but also integrates Explainable Artificial Intelligence, Blockchain technology, Differential Privacy and Post-Quantum Cryptography in one Healthcare Intelligence Ecosystem. The proposed framework involves using the classical AI models such as CNN, LSTM, Random Forest, and XGBoost. When quantum analytics is used in more complex contexts, such as in the health sector, quantum support vector machines, variational quantum classifiers and quantum neural networks are used. The explainability capacities of SHAP and LIME make it attainable to boost clinical trust and CRYSTALS-Kyber and CRYSTALS-Dilithium would help safeguard Quantum PC data.