A recent study highlights the ongoing challenges of reproducibility in deep learning model training, particularly in histopathology. By training a CLIP model on the QUILT-1M dataset, researchers explored how varying hyperparameter settings and augmentation strategies affect outcomes across three different histopathology datasets. This research is crucial as it addresses the inconsistencies that can arise from software randomness and hardware variability, ultimately aiming to improve the reliability of deep learning applications in medical diagnostics.