The Robustness principle refers to the ability of a medical AI model to maintain its performance and accuracy when it is applied under highly variable conditions in the real world, outside the controlled environment of the laboratory where the algorithm was built. Medical variations are an integral part of real-world radiology, and given the differences in clinical practices between radiology departments within as well as across centres and countries, it is important to implement preventive and corrective measures to enhance the robustness of the AI algorithms against changing clinical conditions. The robustness of a model is, hence, defined by its capability to generalise and predict well even in the presence of variable conditions causing domain and dataset shifts that may or may not have been anticipated before deployment. To assess and achieve robustness of medical AI algorithms, we propose the following recommendations: