Key Success Factors for Successful Implementation of AI Based Segmentation Algorithms in Clinical Radiology Practice | Biomedgrid

With the rise of large amounts of patient's clinical and imaging data, the development of artificial intelligence tools based on machine learning and deep learning capable of performing several tasks such as image classification or regression, organ segmentation or feature extraction, has soared over the past few years [1]. These developments create many opportunities for radiologists and are likely to impact their routine practice in the long run by providing tools that will improve the accuracy and efficiency of diagnosis and prognosis. It will arguably allow radiologists to spend more time on complex problem solving by rebarbative tasks and help grasping more useful information from medical images [2]. Despite a great research interest, many challenges are getting in the way of an efficient, safe and ethical implementation of those tools in radiologists' daily practice [3]. Nevertheless, not all tasks and modalities of medical AI have reached the same level of maturity nor are they developing at the same pace.