Scaling AI Projects for Radiology-Causes and Consequences

Abstract

Artificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging examinations. However, the implementation of AI for clinical practice has not lived up to expectations. We suggest that a key problem with AI projects in radiology is that high expectations associated with new and unproven AI technology tend to scale the projects in ways that challenge their anchoring in local practice and their initial purpose of serving local needs. Empirically, we focus on the procurement of an AI solution for radiology practice at a large health trust in Norway where it was intended that AI technology would be used to process the screening of images more effectively. Theoretically, we draw on the information infrastructure literature, which is concerned with scaling innovative technologies from local settings, with a limited number of users, to broad-use contexts with many users.