AI models have demonstrated remarkable success in the healthcare domain, spanning from medical imaging to signal
processing. Nevertheless, as the complexity of these models grows it becomes increasingly challenging to understand
and trust them. To effectively deploy these models in real-world medical scenarios, there is a critical need for
transparency and understanding of the underlying processes and insights learned by these black-box algorithms. This
necessity has led to a wave of research that aims at developing methods and techniques that enhance the explainability
of deep learning methods tailored to the healthcare domain. This session seeks to bring together experts in the field of
biomedical images and signals processing and XAI in order to showcase the latest advancements in the intersection of
these research areas to demystify AI decision-making processes in healthcare.

Paper submission guidelines: https://www.siren-neural-net.it/wirn-2024