ReviewDiagnostics (Basel, Switzerland)2024
Artificial Intelligence in Uropathology.
Review in Diagnostics (Basel, Switzerland), 2024. 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.
- Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Optimizing clinical risk stratification of localized prostate cancer.Current opinion in urology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.
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.