This study addressed the instability of AI models used in embryo selection, which can be sensitive to small, irrelevant changes in input images. A deep learning model was tested on various image modifications, such as rotation and brightness shifts, and applied techniques like ensembling and robust training. These methods reduced score variability by 86% without affecting predictive accuracy. The findings highlight that AI tools can be made more consistent and reliable for clinical use in IVF.