  The Segment Anything Model (SAM) is a new image segmentation tool trained
with the largest available segmentation dataset. The model has demonstrated
that, with prompts, it can create high-quality masks for general images.
However, the performance of the model on medical images requires further
validation. To assist with the development, assessment, and application of SAM
on medical images, we introduce Segment Any Medical Model (SAMM), an extension
of SAM on 3D Slicer - an image processing and visualization software
extensively used by the medical imaging community. This open-source extension
to 3D Slicer and its demonstrations are posted on GitHub
(https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a
complete cycle and can infer image masks in nearly real-time.
