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The aim is to evaluate the effectiveness of vision language models in analyzing microscopy images for various tasks.

Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges

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The aim is to evaluate the effectiveness of vision language models in analyzing microscopy images for various tasks.
Assessment of models including ChatGPT-5, Gemini-2.5Pro, Llama-3.2V, LLaVA-1.5, and SAM-2.
Tasks include classification, segmentation, counting, and visual question answering using microscopy images.
Comparison of performance against prior model versions and domain expert accuracy.
ChatGPT and Gemini excelled in comprehending microscopy images, achieving higher scores.
SAM demonstrated strong performance in object isolation and segmentation tasks.
Despite improvements, performance did not reach domain expert accuracy, especially with complex images.

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Evaluation assesses vision language models' capabilities in analyzing microscopy images, highlighting challenges and opportunities.

Verma et al. (2026) studied this question.

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