ReviewAmerican journal of clinical and experimental urology2020
A review of current advancements and limitations of artificial intelligence in genitourinary cancers.
Review in American journal of clinical and experimental urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Article
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Transforming Pediatric Urology With Artificial Intelligence: A Narrative Review of Current Evidence and Practice.Cureus · 2026Review
- AI-PEDURO - Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository.Journal of pediatric urology · 2025Article
- Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound.Investigative and clinical urology · 2023Article
- Artificial intelligence in academic writing: a paradigm-shifting technological advance.Nature reviews. Urology · 2023Article
- In response to Korreman s. et al. Radiation oncologists are, above all, medical doctors.Clinical and translational radiation oncology · 2021Article
- The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Advances in deep learning and neural networking have allowed clinicians to understand the impact that artificial intelligence (AI) could have on improving clinical outcomes and resources expenditures. In the realm of genitourinary (GU) cancers, AI has had particular success in improving the diagnosis and treatment of prostate, renal, and bladder cancers. Numerous studies have developed methods to utilize neural networks to automate prognosis prediction, treatment plan optimization, and patient education. Furthermore, many groups have explored other techniques, including digital pathology and expert 3D modeling systems. Compared to established methods, nearly all the studies showed some level of improvement and there is evidence that AI pipelines can reduce the subjectivity in the diagnosis and management of GU malignancies. However, despite the many potential benefits of utilizing AI in urologic oncology, there are some notable limitations of AI when combating real-world data sets. Thus, it is vital that more prospective studies be conducted that will allow for a better understanding of the benefits of AI to both cancer patients and urologists.
Indexed as
Identifiers
33235893PMC7677518What 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.