In a recent study published in Science Advances, a team of researchers from the National Research Council's Institute of Cultural Heritage Sciences (CNR-ISPC) introduced an innovative method, based on Artificial Intelligence, for analyzing spectral data obtained through the Macro X-ray Fluorescence (MA-XRF) technique applied to the study of paintings. The new approach was applied, as a pilot case, to MA-XRF data from two surviving fragments of the Baronci Altarpiece painted by Raphael in the 16th century and exhibited at the Capodimonte Museum in Naples. In recent years, technological advances in noninvasive imaging techniques applied to the study and conservation of paintings have fostered the emergence of new advanced computational methods capable of rapid and accurate analysis of the large amounts of data generated in individual measurements. MA-XRF, considered today a fundamental tool for the analysis of such artifacts, allows images of pigment distributions on the paint support to be generated noninvasively, providing valuable information to deepen knowledge of the work, understand the artist's creative process and assess its state of conservation.