Artificial Intelligence (AI) and Deep Learning (DL) hold great promise to transform pathology practice. Currently, the vast majority of commercially available products and AI research focuses on cancer use cases and end-to-end AI, i.e., an approach in which the model learns all the steps between the initial input and the final output, providing qualitative or semiquantitative (class) data. A complementary or alternative approach to analyze histomorphology is using DL-based segmentation of relevant histological compartments and cells, followed by the extraction of relevant quantitative data (features). If done on a large scale, it is termed pathomics, representing a novel -omics approach for morphology at the microscopical level. Pathomics complements molecular omics, like genomics or transcriptomics, or radiomics, which aims at quantifying radiology images at the macroscopic level. This lecture will explore the potential of AI and pathomics in kidney pathology.