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 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 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.
The rapid evolution of Internet of Things (IoT)-enabled healthcare systems has significantly increased the generation, transmission, and storage of medical images such as MRI, CT scans, and X-rays in cloud environments. While this advancement enhances remote diagnostics and telemedicine, it also introduces critical security challenges related to confidentiality, integrity, and privacy of sensitive patient data. Traditional cryptographic methods, including AES and RSA, are often inadequate in addressing the complex requirements of medical image security due to high computational overhead and vulnerability to emerging cyber threats. Consequently, recent research has shifted toward hybrid approaches integrating chaos theory, DNA encoding, deep learning, and quantum computing. This survey paper provides a comprehensive analysis of modern techniques and architectures for secure medical image cryptanalysis, with a particular focus on quantum neural networks (QNNs) in IoT-enabled cloud environments. The study reviews state-of-the-art encryption schemes such as hyperchaotic systems, quantum key distribution, and hybrid quantum-classical cryptography, highlighting their effectiveness in securing medical image transmission and storage. Additionally, the role of deep learning-based cryptographic models and quantum-inspired algorithms in improving robustness against attacks is examined. The paper further presents a comparative analysis of recent studies (2020–2023), identifying key performance metrics such as entropy, PSNR, computational efficiency, and resistance to statistical and differential attacks. Challenges including resource constraints of IoT devices, scalability issues, and limitations of current quantum hardware are also discussed. Finally, the paper outlines future research directions emphasizing the integration of quantum neural networks and lightweight cryptographic frameworks to ensure secure, efficient, and scalable healthcare systems.
The increasing digitalization of the healthcare system, driven by electronic health records, medical IoT devices, and AI-aided diagnostics, has significantly expanded the attack surface for cyber threats. Although current cryptographic protocols provide sufficient security against classical adversaries, new developments in quantum computing threaten the confidentiality, integrity, and availability of medical information as never before. Shor’s algorithm could break widely used asymmetric encryption systems, and Grover’s algorithm weakens symmetric ones, which could render modern healthcare security systems obsolete. This review provides an in-depth and organized analysis of the evolving quantum threat environment in healthcare ecosystems and of the need to move toward quantum-resilient security architectures. It synthesizes the existing literature on post-quantum cryptography (PQC) and identifies lattice-based, hash-based, multivariate, and code-based algorithms as potentially viable long-term replacements. Moreover, it examines the cybersecurity issues that quantum-enabled attacks pose to medical IoT systems, clinical decision-support systems, wearable sensors, telemedicine systems, and cloud-based health information exchanges. The review also covers hybrid quantum-classical defenses, quantum key distribution (QKD), quantum random number generation (QRNG), and blockchain-based trust systems that aim to make systems resilient to advanced persistent quantum attacks. This paper identifies key gaps and outlines future research directions for developing quantum-secure medical systems. The results highlight the importance of early adoption of PQC standards and a layered cyber defense approach to protect patient privacy, ensure regulatory compliance, and retain trust in next-generation digital medical data communication.
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 computing is quickly revolutionizing medicine, promising unprecedented levels of processing, analysis, and decision-making abilities. This review discusses recent developments in applying quantum computing to medical imaging, diagnostics, and treatment planning. Quantum machine learning and hybrid quantum-classical models have been used by researchers to show improved accuracy, speed, and scalability in medical image classification, segmentation, and disease prediction. Quantum-improved algorithms are being implemented for early detection of multifactorial diseases like cancer and neurologic disorders with potential for advancing clinical prognoses and biomarker discovery. Moreover, quantum cryptography methods are under development to enhance security for private medical information with concerns regarding expanding privacy and security risks. Improvements in quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) also hold promise in enhancing diagnostic efficiency and resource savings. This review synthesizes existing applications, identifies new trends, and delineates challenges and future directions of quantum computing in medicine. It underscores the importance of explainable and interpretable quantum AI systems to maintain clinical trust and utility. The results indicate that quantum technologies, although in their infancy, have transformative potential for precision medicine, personalized therapy, and sophisticated medical decision-making.
Quantum image encryption has become a promising direction to protect visual information stored or transmitted through cyberspace due to its cryptographic power and resilience that are under-researched. This survey makes a systematic study of the current advancement in the field of quan-tum image encryption concerning cryptographic security and AI-based approaches. We survey the state of quantum-based encryption, such as quantum key distribution, chaotic quantum systems, and AI methods, such as deep learning, to generate keys and detect attacks through a thorough literature review. The significant findings include that quantum encryption may provide unprecedented security levels through mechanisms such as superposition and entanglement, yet computational complexity and hardware constraints are obstacles to implementation. AI-based methods have improved efficiency and adaptability but have created adversarial attack risks. We can conclude that quantum image encryption has the potential to transform the secure transmission of data, but it is necessary to incorporate AI-powered optimizations into the proven quantum systems to ensure scalability and resistance to evolving threats. Hybrid models are the way of the future as they need to find a balance between security and realistic usage.