Promptable foundation models (FMs), such as the Segment Anything Model (SAM), offer a powerful new paradigm for image and video segmentation by using flexible user prompts—like points or bounding boxes—to guide object identification. While these models have shown strong generalization in natural image domains, their effectiveness in clinical settings, particularly medical imaging, requires further investigation.