Type 2 Diabetic Nephropathy (T2DN), in the setting of type 2 diabetes, is the world占쎌뀼 leading cause of chronic kidney disease and end-stage kidney disease (ESKD). The increasing prevalence and heterogeneous phenotype of T2DN complicate the approach to treating patients. While kidney biopsy is the gold standard for exclusion of non-DN diagnoses and confirming diagnosis of DN, it is imperfect in predicting progression to ESKD. Artificial intelligence (AI) has the potential to improve classification of T2DN, predict progression risk, and via integration with urinary proteomic profiles identify novel urinary biomarkers, taken together augmenting and going beyond current pathology practice.