The increasing use of Clinical Artificial Intelligence/Machine Learning (AI/ML)-enabled Software as a Medical Device (SaMD) for healthcare applications, including medical imaging, is posing significant challenges for regulatory bodies in ensuring that these devices are valid, robust, transparent, explainable, fair, safe, and accurate. One of the major challenges is the phenomenon of data shift, which refers to a mismatch between the distribution of the data that was used for model training/testing and the distribution of the data to which the model was applied. This makes it difficult to generalize AI/ML-enabled SaMD across different healthcare institutions, different medical devices, and disease patterns, resulting in AI model performance deterioration, erroneous outputs, and adverse patient outcomes.