  Vision language models (VLMs) have recently emerged and gained the spotlight
for their ability to comprehend the dual modality of image and textual data.
VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive
performance on tasks such as natural image captioning, visual question
answering (VQA), and spatial reasoning. Additionally, a universal segmentation
model by Meta AI, Segment Anything Model (SAM) shows unprecedented performance
at isolating objects from unforeseen images. Since medical experts, biologists,
and materials scientists routinely examine microscopy or medical images in
conjunction with textual information in the form of captions, literature, or
reports, and draw conclusions of great importance and merit, it is indubitably
essential to test the performance of VLMs and foundation models such as SAM, on
these images. In this study, we charge ChatGPT, LLaVA, Gemini, and SAM with
classification, segmentation, counting, and VQA tasks on a variety of
microscopy images. We observe that ChatGPT and Gemini are impressively able to
comprehend the visual features in microscopy images, while SAM is quite capable
at isolating artefacts in a general sense. However, the performance is not
close to that of a domain expert - the models are readily encumbered by the
introduction of impurities, defects, artefact overlaps and diversity present in
the images.
