Introduction: Technological progress has encouraged the increasing use of radiological imaging, and the increase in their number leads to an increased workload for radiologists. Therefore, the driver of the application of artificial intelligence in radiology is considered to be precisely the reduction of the workload of radiologists and the need for ever greater precision and efficiency. Aim: The aim of this paper is to bring the reader closer to artificial intelligence itself and its application in radiology, especially in magnetic resonance modalities, and how deep learning algorithms accelerate image reconstruction and lead to faster and more accurate results. Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and also more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation achieved, above all, high accuracy and precision, objectivity and exceptional time saving. Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.