ReviewHistopathology2026
Artificial intelligence in genitourinary pathology.
Review in Histopathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Generalizability of a biopsy-trained artificial intelligence algorithm to TURP and HoLEP specimens for detection of incidental prostate adenocarcinoma.Diagnostic pathology · 2026Article
- Artificial intelligence and machine learning in neurogenic lower urinary tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives.Frontiers in urology · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is now a practical, value-generating tool in genitourinary (GU) pathology. Real-world deployments report up to 65% time-savings and multi-million-dollar returns on investment within 3 years at high-volume centres. Across prostate, bladder, renal and testicular systems, contemporary algorithms equal or exceed expert accuracy for cancer detection, grading and prognostication. Foundation models trained on millions of whole-slide images now match specialized organ-specific tools without bespoke tuning. High AI-pathologist concordance is widely regarded as a surrogate marker of safety and clinical acceptability, yet no universally codified regulatory threshold for sensitivity, specificity or concordance has been issued. Because internationally recognized guidelines still omit detailed instructions for safe roll-out and sustained performance, we distilled insights from real-world deployments and pioneering pilot studies into two complementary roadmaps: the nine-step VALIDATED framework, which focuses on governance and safety oversight, and the 11-principle ORCHESTRATE blueprint, which guides day-to-day implementation. By 2030, we anticipate AI will automate ~80% of routine quantification, allowing pathologists to assume the role of diagnostic orchestrators who integrate multimodal data streams, helping offset a ~40% workforce shortfall and reducing inter-observer variability across practice settings. This review distils the evidence, economics and practical guidance required for successful AI adoption in GU pathology. Institutions following the VALIDATED-ORCHESTRATE pathway can harness efficiency gains while maintaining diagnostic excellence and achieving positive ROI within 5 years.
Indexed as
Identifiers
What OpenQuestion holds
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.