Deep learning in radiology is a rapidly expanding research area that focuses on applying neural network models to medical imaging for improved diagnosis, detection, and prognosis of diseases. Early approaches employed convolutional neural networks (CNNs) for tasks such as image classification, lesion detection, and organ segmentation in modalities like X-ray, CT, MRI, and ultrasound. Subsequent research introduced advanced architectures including U-Net for precise segmentation, attention mechanisms for highlighting relevant regions, and 3D-CNNs for volumetric imaging analysis. Recent studies also leverage transformer-based models, multi-modal learning combining imaging with clinical data, and generative models for data augmentation and reconstruction. Applications span disease detection (e.g., cancer, pneumonia, brain disorders), workflow optimization, predictive modeling, and radiomics feature extraction. Current research emphasizes explainability, robustness to noise and domain shifts, integration with electronic health records, and deployment in real-time clinical settings, making deep learning a transformative tool in modern radiology for enhanced accuracy, efficiency, and personalized medicine.