  Artificial intelligence has become pervasive across disciplines and fields,
and biomedical image and signal processing is no exception. The growing and
widespread interest on the topic has triggered a vast research activity that is
reflected in an exponential research effort. Through study of massive and
diverse biomedical data, machine and deep learning models have revolutionized
various tasks such as modeling, segmentation, registration, classification and
synthesis, outperforming traditional techniques. However, the difficulty in
translating the results into biologically/clinically interpretable information
is preventing their full exploitation in the field. Explainable AI (XAI)
attempts to fill this translational gap by providing means to make the models
interpretable and providing explanations. Different solutions have been
proposed so far and are gaining increasing interest from the community. This
paper aims at providing an overview on XAI in biomedical data processing and
points to an upcoming Special Issue on Deep Learning in Biomedical Image and
Signal Processing of the IEEE Signal Processing Magazine that is going to
appear in March 2022.
