Technological advances in Artificial Intelligence (AI), particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in various medical fields, propelling it forward at a rapid pace. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of deep learning, radiology, oncology, cardiology, bioinformatics, and data science. Also, he will explore how these methods could impact multiple facets of medicine, with a general focus on applications in radiology, and demonstrate ways in which these methods are advancing the field. The presentation will conclude with a discussion on the need for open-source deep learning frameworks that are transparent and reproducible.