  Recent advances in segmentation foundation models have enabled accurate and
efficient segmentation across a wide range of natural images and videos, but
their utility to medical data remains unclear. In this work, we first present a
comprehensive benchmarking of the Segment Anything Model 2 (SAM2) across 11
medical image modalities and videos and point out its strengths and weaknesses
by comparing it to SAM1 and MedSAM. Then, we develop a transfer learning
pipeline and demonstrate SAM2 can be quickly adapted to medical domain by
fine-tuning. Furthermore, we implement SAM2 as a 3D slicer plugin and Gradio
API for efficient 3D image and video segmentation. The code has been made
publicly available at \url{https://github.com/bowang-lab/MedSAM}.
