@Radiology_AI

Over the last several years, artificial intelligence (AI) has become one of the highest profile topics in radiology, recognized in part by the creation of this journal (1). This focus and interest has been driven largely by the potential AI shows to broadly change the way we practice radiology across every subspecialty. That potential has been demonstrated by a flood of manuscripts describing technical advances, algorithms, and proofs of concept aimed at a wide variety of radiologic tasks. However, no amount of demonstrated potential has a direct impact on patient care or clinical practice; achieving such an impact requires moving beyond the creation of AI to the deployment of AI into clinical environments for routine use. It is probably not surprising to those who practice radiology or work in radiology information technology that achieving this translational goal is challenging and has occurred at a much slower pace than suggested by some who feverishly predicted that AI would bring an end to radiology as a profession in a few short years.