Special Issue Information:
Background and Motivation:
The rapid digitization of healthcare systems has driven an unprecedented reliance on Medical Imaging (MI) technologies such as X-rays, Ultrasound, Computed Tomography (CT), and Medical Resonance Imaging (MRI) for accurate diagnosis, treatment planning, and disease monitoring. With the proliferation of telemedicine and Internet of Medical Things (IoMT) devices, medical images are increasingly transmitted, analyzed, and stored across interconnected networks. While this shift enables real-time collaboration and improved patient outcomes, it also raises necessary concerns about data security, privacy, and authentication. Sensitive patient information is vulnerable to cyber threats, unauthorized access, and data tampering, which can undermine the trustworthiness of healthcare systems and violate stringent regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Emerging technologies such as Blockchain (BT), Federated Learning (FL), Quantum Machine Learning (QML), and Digital Twin (DT) services offer transformative solutions for addressing these challenges. BT provides immutable and decentralized ledgers for secure medical image authentication and provenance tracking. FL enables collaborative model training on distributed datasets without compromising data privacy, making it ideal for multi-institutional medical imaging studies. QML holds the potential to revolutionize the computational aspects of medical imaging, offering enhanced security and faster analytics. Meanwhile, DT of imaging workflows can simulate, monitor, and optimize image transmission and storage processes, ensuring efficiency and resilience. In telemedicine, these technologies are pivotal in securely transmitting medical images, enabling privacy-preserving remote diagnosis, and integrating diverse healthcare systems. They not only safeguard sensitive patient data but also ensure its integrity and availability, fostering patient and provider trust in digital healthcare ecosystems.
This Special Issue, titled "Next-Generation Healthcare and Secure Medical Imaging: Advances in Authentication, Privacy, and Data Management," aims to explore and showcase innovative frameworks for secure and privacy-preserving medical imaging. The primary objective is to address the pressing challenges of authentication, privacy, and data management by leveraging advanced technologies such as blockchain, federated learning, quantum machine learning, and digital twins. Researchers and practitioners are invited to present their advancements and applications of these next-generation technologies, bridging the gap between theoretical innovation and practical implementation in healthcare.
Co-Guest Editor(s):
Dr. Rajendrasinh Jadeja,Dr. Manish Kumar
