XAIMed 2.0: Integrating Saliency Maps and Large Language Models for Explainable Diagnostic Support in Medical Imaging

In recent years, Deep Learning (DL) models have shown excellent results in various fields, including healthcare, but their “opaque” nature limits their trust and adoption in critical contexts. In this work, we present XAIMed2.0, an application designed to support healthcare professionals in interpreting the decisions of CNN models applied to medical image analysis. The application integrates several eXplainable AI (XAI) techniques to offer different visual explanations. The application integrates multiple eXplainable AI (XAI) techniques to generate visual explanations. Specifically, it employs several saliency map methods (Grad-CAM, Grad-CAM++, RISE, and LIME) and merges them into a cumulative visualization that highlights the regions of highest consensus across methods. To complement the visual explanations, the system uses a Large Language Model (LLM) to generate textual explanations in natural language, capable of contextualizing the results, highlighting discrepancies, and improving clinician understanding.