The article introduces XCoOp, a novel explainable prompt learning framework for computer-aided diagnosis. It leverages medical knowledge to align semantics of images, prompts, and clinical concepts for enhanced interpretability. By addressing the lack of valuable concept annotations, XCoOp offers visual and textual explanations for prompts. Extensive experiments demonstrate superior diagnostic performance, flexibility, and interpretability. The framework highlights the effectiveness of foundation models in facilitating Explainable Artificial Intelligence (XAI) in high-stakes scenarios like healthcare.
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