Cutting-edge deep learning architectures such as U-Net, ResNet, and Conditional Generative Adversarial Networks (CGANs) have been employed to enhance medical imaging quality and diagnostic accuracy. Applications include improved hippocampal segmentation for Alzheimer’s prediction, nasopharyngeal endoscopy image analysis, and cone-beam CT image enhancement. Furthermore, machine learning models predict radiation-induced skin toxicity in breast cancer patients and facilitate kidney stone diagnosis via radiographic imaging. These developments signify growing integration of AI to support clinicians with precise and early detection methods.