  Due to the inherent flexibility of prompting, foundation models have emerged
as the predominant force in the fields of natural language processing and
computer vision. The recent introduction of the Segment Anything Model (SAM)
signifies a noteworthy expansion of the prompt-driven paradigm into the domain
of image segmentation, thereby introducing a plethora of previously unexplored
capabilities. However, the viability of its application to medical image
segmentation remains uncertain, given the substantial distinctions between
natural and medical images. In this work, we provide a comprehensive overview
of recent endeavors aimed at extending the efficacy of SAM to medical image
segmentation tasks, encompassing both empirical benchmarking and methodological
adaptations. Additionally, we explore potential avenues for future research
directions in SAM's role within medical image segmentation. While direct
application of SAM to medical image segmentation does not yield satisfactory
performance on multi-modal and multi-target medical datasets so far, numerous
insights gleaned from these efforts serve as valuable guidance for shaping the
trajectory of foundational models in the realm of medical image analysis. To
support ongoing research endeavors, we maintain an active repository that
contains an up-to-date paper list and a succinct summary of open-source
projects at https://github.com/YichiZhang98/SAM4MIS.
