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ConceptSMILE is a perturbation-based auditing framework for evaluating the reliability of concept-based explainable AI, tested on retinal fundus images.
This paper evaluates the zero-shot capability of multimodal large language models (MLLMs) for localized concept naming in images, proposing a reproducible evaluation protocol that achieves 62-88% object-level accuracy without training.