  The introduction of saliency map algorithms as an approach for assessing the
interoperability of images has allowed for a deeper understanding of current
black-box models with Artificial Intelligence. Their rise in popularity has led
to these algorithms being applied in multiple fields, including medical
imaging. With a classification task as important as those in the medical
domain, a need for rigorous testing of their capabilities arises. Current works
examine capabilities through assessing the localization of saliency maps upon
medical abnormalities within an image, through comparisons with human
annotations. We propose utilizing Segment Anything Model (SAM) to both further
the accuracy of such existing metrics, while also generalizing beyond the need
for human annotations. Our results show both high degrees of similarity to
existing metrics while also highlighting the capabilities of this methodology
to beyond human-annotation. Furthermore, we explore the applications (and
challenges) of SAM within the medical domain, including image pre-processing
before segmenting, natural language proposals to SAM in the form of CLIP-SAM,
and SAM accuracy across multiple medical imaging datasets.
