ReviewBiomolecules & biomedicine2023
Artificial intelligence in renal pathology: Current status and future.
Review in Biomolecules & biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- [Histology and more-modern nephropathology].Innere Medizin (Heidelberg, Germany) · 2026Review
- Renal-AI: A Deep Learning Platform for Multi-Scale Detection of Renal Ultrastructural Features in Electron Microscopy Images.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Kidney diseases and single-cell sequencing research: a bibliometric analysis from 2015 to 2024.Renal failure · 2025Review
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- AI-Based System for Analysis of Electron Microscope Images in Glomerular Disease.JAMA network open · 2025Article
- Histopathology of Diabetic Nephropathy: Beyond Glomerular Basement Membrane Thickening.Cureus · 2025Review
- Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath.American journal of physiology. Renal physiology · 2025Article
- Revolutionizing renal biopsy: the emerging role of omics and artificial intelligence in nephrology.Frontiers in medicine · 2025Review
- GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning.Computational and structural biotechnology journal · 2025Article
- The Role of Artificial Intelligence in Nephrology Clinical Trials.Journal of the American Society of Nephrology : JASN · 2024Article
- Vision transformer introduces a new vitality to the classification of renal pathology.BMC nephrology · 2024Article
- How artificial intelligence is transforming nephrology.BMC nephrology · 2024Article
- Clinicopathological Pattern of Renal Biopsies in Children with Nephrotic Syndrome.Clinical medicine & research · 2024Article
- PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Urinary podocyte markers of disease activity, therapeutic efficacy, and long-term outcomes in acute and chronic kidney diseases.Clinical and experimental nephrology · 2024Review
- Magnetic Resonance Imaging in Atherosclerotic Renal Artery Stenosis: The Update and Future Directions from Interventional Perspective.Kidney diseases (Basel, Switzerland) · 2024Review
- Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Advances in artificial intelligence (AI) along with the growing digitization of pathology slides for diagnosis are promising approach to meet the demand for more accurate detection, classification, and prediction of the outcome of renal pathology. AI has contributed substantially to a variety of clinical applications, including renal pathology. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being used in multiple areas of pathology. In this narrative review, we first provide a general description of AI methods, and then discuss the current and prospective applications of AI in the field of renal pathology. Both diagnostic and predictive prognostic applications are covered, emphasizing AI in renal pathology images, predictive models, and 3D in renal pathology. Finally, we outline the challenges associated with the implementation of AI platforms in renal pathology and provide our perspective on how these platforms might change in this field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.