Recent studies highlight the rapid integration of deep learning and artificial intelligence across medical imaging and diagnostics. Advances include enhanced cranial ultrasound methods for brain hemorrhage detection, automated anatomical measurements, and AI models predicting seizure risk or cancer outcomes. Institutions such as University of Virginia and multiple pediatric radiology centers are pioneering these computational approaches to improve diagnostic accuracy, efficiency, and clinical decision support, signaling a transformative shift towards AI-led medical workflows.