BJU international
June 26, 2026 · via Europe PMC
Artificial intelligence in genitourinary oncology: publication trends and systematic review.
Kim A, Moore NA, Dong T, Fu K, Keyser K, Clifton S, Ayanambakkam A, Nipp R.
AI summary — for doctors, generated from the abstract only
A systematic review found that artificial intelligence research in genitourinary cancer has grown substantially over the past decade, but high-quality evidence from randomized trials remains very limited, with only 4 such trials identified out of over 1,200 relevant studies.
What was studied
A publication trend analysis and systematic review of randomized controlled trials examining artificial intelligence applications in genitourinary oncology (prostate, kidney, and bladder cancers), searching literature from 2013 to 2023 and assessing risk of bias in the identified randomized trials using the Cochrane tool.
Key findings
- Of 2,409 articles identified, 1,220 met inclusion criteria, including 962 retrospective studies, 175 prospective studies, and only 4 randomized controlled trials
- Studies most commonly addressed prostate cancer (923 studies), followed by kidney cancer (274) and urothelial (bladder) cancer (194)
- Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration starting in 2019
- The 4 randomized trials (1 urothelial, 3 prostate) showed improved performance of AI-based diagnostics over conventional methods and gains in imaging interpretation and operational efficiency for prognostication and treatment planning uses
Clinical relevance
This review suggests that while AI shows promise across diagnostics, prognostication, and treatment planning in genitourinary cancers, clinicians should be aware that the evidence base supporting routine clinical use remains limited, mostly based on retrospective rather than randomized trial data.
Who should care
Limitations
The authors explicitly note that high-level evidence from randomized controlled trials remains very limited (only 4 identified), and risk of bias varied across the broader literature, particularly related to randomization and deviations from intended interventions.
AI research in genitourinary cancer has grown rapidly, but strong evidence from randomized trials remains scarce, with only 4 such trials found despite more than 1,200 relevant studies overall.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objective
To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication.
Methods
We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool.
Results
Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions.
Conclusions
Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.
DOI
10.1111/bju.70361
PMID
42360093
PMCID
PMC13550791
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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