Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis
In brief
AI classified three common kidney diseases with 94% to 96% accuracy
Across 15 studies, pathology-based AI models had pooled accuracy of 94% for IgA nephropathy and 96% for membranous nephropathy and minimal change disease. AI also scored higher than senior pathologists for these diseases in limited comparisons, but most studies were retrospective and varied widely; whether AI improves diagnosis in real-world practice remains unknown.
- Journal
- Renal failure (Q1)
- Published
- 6 October 2026
- Study design
- Systematic review of cohort studies
- Evidence level
- Level 2, Moderate (CEBM 2a)
- Authors
- Min Tong, Yingying Jiang, Rongxin Zhu, Zichao Ding, Diya Ma, Jingdan Yin, et al.
- PMID
- 42838512
- DOI
- 10.1080/0886022X.2026.2729643
Why clinicians should know about it
- Picked for Pathology and Forensic Medicine (paper of the day, 7 October 2026): AI‑powered renal pathology for glomerular disease classification
Abstract
BACKGROUND: Diagnostic performance varies across glomerular disease (GD) subtypes, and whether artificial intelligence (AI) outperforms pathologists with different experience levels remains uncertain. OBJECTIVE: To evaluate pathology-based AI models for GD classification and compare their performance with pathologists. METHODS: 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). RESULTS: Fifteen studies comprising 39,536 validation sample units, not necessarily unique patients, were included. For subtypes with at least 10 validation datasets, AI achieved high performance for membranous nephropathy (MN; sensitivity 0.96, precision 0.94, accuracy 0.96, F1 score 0.95, AUC 0.98), IgA nephropathy (IgAN; sensitivity 0.92, precision 0.91, accuracy 0.94, F1 score 0.90, AUC 0.96), and minimal change disease (MCD; sensitivity 0.92, precision 0.87, accuracy 0.96, F1 score 0.89, AUC 1.00). AI also showed higher accuracy than senior pathologists for IgAN, MN, and MCD; however, comparator evidence was sparse and should be interpreted cautiously. Most included studies were retrospective, and substantial heterogeneity was observed across datasets, imaging modalities, model architectures, and validation strategies. CONCLUSIONS: Pathology-based AI shows strong potential for GD classification, but current head-to-head evidence is insufficient to establish superiority over pathologists, particularly senior pathologists. Prospective multicenter studies integrating multimodal clinical data and standardized external validation are needed.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.