Medical image diagnosis has become an integral part of the medical field, which helps the doctor diagnose the disease using various medical image techniques like X-ray, computed tomography, and magnetic resonance imaging. The recent advancements in artificial intelligence and deep learning techniques have improved the efficiency of the medical image diagnosis using artificial intelligence and machine learning techniques. However, the large-scale requirement of the data has raised many issues related to the privacy and security of the data in the medical field. In this regard, the concept of privacy-preserving artificial intelligence has been introduced to ensure the security of the data in the artificial intelligence system. Classical machine learning techniques are limited in handling the complexity of the data and the efficiency of the system. In this regard, the concept of quantum computing has been introduced, which helps the system process the data using the superposition property of the system. The proposed approach also integrates principles of data science and cyber security to ensure efficient data handling and robust protection against emerging digital threats
Medical image diagnosis has become an integral part of the medical field, which helps the doctor diagnose the disease using various medical image techniques like X-ray, computed tomography, and magnetic resonance imaging. The recent advancements in artificial intelligence and deep learning techniques have improved the efficiency of the medical image diagnosis using artificial intelligence and machine learning techniques. However, the large-scale requirement of the data has raised many issues related to the privacy and security of the data in the medical field. In this regard, the concept of privacy-preserving artificial intelligence has been introduced to ensure the security of the data in the artificial intelligence system. Classical machine learning techniques are limited in handling the complexity of the data and the efficiency of the system. In this regard, the concept of quantum computing has been introduced, which helps the system process the data using the superposition property of the system. The proposed approach also integrates principles of data science and cyber security to ensure efficient data handling and robust protection against emerging digital threats
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.
The rapid growth of IoT-enabled healthcare systems and cloud storage technologies has significantly increased the reliance on digital medical imaging for diagnosis and treatment. However, the storage and transmission of medical images over cloud platforms introduce serious security and privacy concerns, particularly due to cyberattacks, data breaches, and adversarial manipulations. Medical images, including MRI, CT, and X-ray scans, contain sensitive patient information that must be protected during storage and communication. Traditional cryptographic techniques, while effective, are increasingly vulnerable to emerging threats, especially with the advent of quantum computing. Recent advances in Artificial Intelligence (AI) and Quantum Neural Networks (QNNs) have opened new possibilities for secure medical image cryptanalysis and protection. Deep learning-based cryptographic techniques have demonstrated the ability to enhance image encryption, authentication, and secure transmission while maintaining diagnostic quality. Additionally, quantum computing introduces advanced cryptographic paradigms such as Quantum Key Distribution (QKD) and quantum-enhanced encryption, which significantly improve data security in IoT-enabled cloud environments. Quantum neural networks further enhance cryptographic systems by enabling efficient processing of high-dimensional medical image data while ensuring privacy preservation through quantum cryptography mechanisms. Moreover, recent studies highlight that adversarial attacks on AI-based medical imaging systems can lead to incorrect diagnoses, emphasizing the need for robust and secure architectures. This paper presents a comprehensive systematic review of recent advances in secure medical image cryptanalysis using quantum neural networks in IoT-enabled cloud storage systems. It examines AI-driven cryptographic techniques, quantum-based security models, and hybrid frameworks, while identifying research trends, challenges, and future directions for developing secure and efficient healthcare systems.
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].
The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.
As the digital transformation accelerates across industries, the need for robust data protection mechanisms has never been more critical. Sensitive data, particularly in sectors like healthcare, finance, and government, is constantly at risk of exposure due to increasing cyber threats. Traditional encryption techniques, while effective, often fall short when dealing with large-scale data and complex threats. This paper explores the intersection of privacy-preserving techniques, quantum computing, and artificial intelligence (AI) to enhance data protection. We propose the integration of quantum-enhanced AI frameworks that combine quantum computing's unique capabilities with AI-driven privacy-preserving mechanisms. These systems can not only safeguard sensitive data but also improve the efficiency of encryption and decryption processes. The paper discusses the potential of quantum-enhanced AI in creating more secure, scalable, and transparent data protection strategies, while addressing the challenges of implementing such advanced technologies, including computational complexity and ethical considerations. By leveraging quantum algorithms and AI techniques such as homomorphic encryption and differential privacy, the proposed solution aims to provide a futureproof approach to protecting sensitive information from increasingly sophisticated threats.
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).