Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis

Diagnostic performance varies across glomerular disease (GD) subtypes, and whether artificial intelligence (AI) outperforms pathologists with different experience levels remains uncertain.

To evaluate pathology-based AI models for GD classification and compare their performance with pathologists.

PubMed, Embase, Web of Science, and Cochrane Library were searched through 15 July 2026. Studies using pathology images and pathology diagnosis as the reference standard were included. Random-effects models pooled sensitivity, precision, accuracy, F1 score and area under the curve (AUC).