Artificial intelligence (AI) is a computer-based system, which in diagnostic imaging can improve patient flow, optimise image processing, shorten scan time, reduce radiation dose and be used as decision aid in image interpretation. In this review, we argue that AI algorithms should be based on evidence with initial hypothesis, then a choice of algorithm and development with training on the initial data set; afterwards the algorithms should be tested on a new representative dataset, and finally it should be tested in a prospective study. If the AI is evidence-based and can solve a task better or cheaper than the usual methodology, it can be implemented.