The advent of artificial intelligence (AI) has brought about a paradigm shift in various fields, including healthcare. In radiology, artificial intelligence (AI) holds significant potential to transform diagnostic procedures, enhancing their accuracy, efficiency, and patient-friendliness. Beyond these benefits, AI may also contribute to mitigating the unsustainable pressures on the current healthcare system, pressures that have been intensified by the rising workload associated with the aging population in Western countries. Despite this potential, AI solutions are only slowly being integrated into clinical practice. A recent publication identified cost and IT integration as primary barriers to AI adoption [
1]. However, the underlying causes of this slow integration extend beyond the financial and performance aspects of AI solutions. …