Under the tremendous success of deep learning technology in various clinical applications, this technology emerged as an effective tool in radiology for classifying radiological data. Deep learning in radiology has grown rapidly in recent years and shows great promise.

Deep learning-based radiology is practically utilized for a variety of categories, such as classification, object detection, semantic segmentation, quantification, image processing, and natural language processing. Hepatology, Cardiology, Neurology, Urology, and Pulmonology are the recent applicative fields of radiology using deep learning.

The significance of deep learning in radiology is high inter-observer reliability, better diagnosing at earlier stages, timely medical decision-making and diagnostic superiority, and improved patient healthcare. Futuristic applications of deep learning in radiology include worklist optimization, NLP, novel diagnostic applications, prognostication, automated tracking of imaging discoveries, and automated preliminary report generation.

Deep learning furnishes prominent opportunities for radiologists to enhance safety by imparting more accurate diagnoses, increasing efficacy by automating medical tasks, and assisting in generating data on imaging features.

Several surveys have been conducted on deep learning-based radiological applications that present implementation, practical consideration, deep learning architectures, performance metrics, benchmark datasets, data collection, current advancements, limitations, ethical issues, and future applications.