  Due to the flexibility of prompting, foundation models have become the
dominant force in the domains of natural language processing and image
generation. With the recent introduction of the Segment Anything Model (SAM),
the prompt-driven paradigm has entered the realm of image segmentation,
bringing with a range of previously unexplored capabilities. However, it
remains unclear whether it can be applicable to medical image segmentation due
to the significant differences between natural images and medical images.In
this work, we summarize recent efforts to extend the success of SAM to medical
image segmentation tasks, including both empirical benchmarking and
methodological adaptations, and discuss potential future directions for SAM in
medical image segmentation. Although directly applying SAM to medical image
segmentation cannot obtain satisfying performance on multi-modal and
multi-target medical datasets, many insights are drawn to guide future research
to develop foundation models for medical image analysis. To facilitate future
research, we maintain an active repository that contains up-to-date paper list
and open-source project summary at https://github.com/YichiZhang98/SAM4MIS.
