Topic: AI-powered Medical Imaging and Diagnosis

Artificial Intelligence (AI) has revolutionized the field of medical imaging and diagnostics, offering innovative solutions for disease detection, prognosis, and treatment planning. AI-driven techniques, such as deep learning, machine learning, and computer vision, have significantly improved the accuracy, efficiency, and automation of medical image analysis. These advancements are transforming radiology, pathology, and various other healthcare domains by enabling faster and more precise diagnoses. Despite its immense potential, AI-powered medical imaging faces several challenges, including data privacy, model interpretability, integration with clinical workflows, and generalizability across diverse patient populations. Addressing these challenges is crucial for the widespread adoption of AI in medical imaging. This Special Issue aims to bring together researchers, practitioners, and industry experts to explore cutting-edge developments in AI-driven medical imaging, covering topics such as deep learning architectures, federated learning, explainable AI (XAI), and multimodal image fusion. We invite original research articles, reviews, and case studies that contribute to the advancement of AI-powered medical imaging and diagnosis, fostering innovation and practical applications in healthcare. Potential topics of this Special Issue include but are not limited to the following:
● Artificial Intelligence in Medical Imaging；
● Deep Learning for Disease Diagnosis;
● Explainable AI (XAI) in Medical Imaging;
● Federated Learning for Medical Data; 
● Privacy-preserving AI in Healthcare;
● Big Data Analytics for Medical Imaging;
● Transfer Learning in Medical Image Analysis.

Artificial Intelligence, medical imaging, deep learning, disease diagnosis, explainable AI (XAI), federated learning, big data analytics, transfer learning