Keeping the Organization in the Loop as a General Concept for Human-Centered AI: The Example of Medical Imaging

Abstract

This study emanates from work on human-centered AI and the claim of “keeping the organiza-tion in the loop”. A previous study suggests a sys-tematic framework of organizational practices in the context of predictive maintenance, and identified four cycles: using AI, customizing AI, original task handling with support of AI, and dealing with con-textual changes. Since we assume that these findings can be generalized for other kinds of applications of Machine Learning (ML), we contrast the manage-ment activities that support the four cycles and their interplay with a widely different domain: the usage of AI for radiology. Our literature analysis reveals a series of overlaps with the existing framework, but also results in the need for extensions, such as holis-tic consideration of workflows or supervision and quality assurance.