ArticleWorld journal of urology2025
Will artificial intelligence (AI) replace cytopathologists: a scoping review of current applications and evidence of A.I. in urine cytology.
Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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
6 citing papers in PubMed.
- The future of endourology is now.World journal of urology · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.Prostate cancer and prostatic diseases · 2026Article
- Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology.iScience · 2026Article
- Artificial Intelligence as a Tool in the Diagnosis of Bladder Cancer: A Narrative Review.Cureus · 2025Review
- ASO Author Reflections: Artificial Intelligence in Bladder Cancer-Transforming Diagnosis and Treatment Paradigms.Annals of surgical oncology · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeUrine cytology, while valuable in facilitating the detection and surveillance of bladder cancer, has notable limitations. The application of artificial intelligence (AI) in urine cytology holds significant promise for improving diagnostic accuracy and efficiency. Our scoping review aims to assess the current evidence of AI's utility in urine cytology.
methodAn electronic literature research on the application of AI in the setting of urine cytology was conducted on PubMed, EMBASE, and Scopus from inception to 1st November 2024. Case reports, abstracts, and reviews were excluded from this analysis. Our search strategy retrieved 1356 articles; after excluding 142 duplicates, the remaining 1214 papers were screened by title and abstract. 31 studies entered full-article review, and a total of 16 articles were included in the final analysis.
resultsThe main application of AI in urine cytology diagnosis is to automate the identification and characterization of abnormal cells. It has also been utilized for risk stratification of abnormal cells, predicting histologic results from urine cytology samples, and predicting bladder cancer recurrence. Current limitation includes the need for robust training datasets and validation studies to ensure the generalizability of AI algorithms.
conclusionIn summary, AI in urine cytology, though still developing, shows significant promise in enhancing diagnostic accuracy and efficiency. Current evidence suggests that AI, as a valuable tool, could revolutionize urinary tract cancer diagnosis and management.
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Registered trials
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