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Foundation models have taken over natural language processing and imagegeneration domains due to the flexibility of prompting. With the recentintroduction of the Segment Anything Model (SAM), this prompt-driven paradigmhas entered image segmentation with a hitherto unexplored abundance ofcapabilities. The purpose of this paper is to conduct an initial evaluation ofthe out-of-the-box zero-shot capabilities of SAM for medical imagesegmentation, by evaluating its performance on an abdominal CT organsegmentation task, via point or bounding box based prompting. We show that SAMgeneralizes well to CT data, making it a potential catalyst for the advancementof semi-automatic segmentation tools for clinicians. We believe that thisfoundation model, while not reaching state-of-the-art segmentation performancein our investigations, can serve as a highly potent starting point for furtheradaptations of such models to the intricacies of the medical domain. Keywords:medical image segmentation, SAM, foundation models, zero-shot learning

SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model