Evidence map›Paper›PMID 42299919›Full record

ReviewBJU international2026

Artificial intelligence in urology: A review of United States Food and Drug Administration-cleared devices.

Zhiyu Qian, Pietro Brin, Stephan Korn, Andrea Cosenza, Hanna Zurl, Klara Pohl, Jina Pakpoor, Caroline M Moore, Francesco Giganti, Quoc-Dien Trinh and 1 more

Abstract readReview
In one paragraph

Review in BJU international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhiyu QianBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-9953-3074
Pietro BrinBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Stephan KornBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Andrea CosenzaDepartment of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.ORCID 0009-0000-5987-4659
Hanna ZurlBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Klara PohlBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Jina PakpoorDepartment of Radiology, University College London Hospitals Trust, London, UK.
Caroline M MooreDivision of Surgical and Interventional Science, University College London, London, UK.
Francesco GigantiDepartment of Radiology, University College London Hospitals Trust, London, UK.
Quoc-Dien TrinhDepartment of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Alexander P ColeDivision of Urology, Maine Health Pen Bay Hospital, Rockport, ME, USA.ORCID 0000-0002-0049-8529

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo systematically characterise United States Food and Drug Administration (FDA) authorised urology-specific artificial intelligence (AI)-enabled medical devices and to evaluate the publicly available evidence supporting their authorisation.

methodsWe reviewed the FDA AI-Enabled Medical Devices List. Devices cleared by September 2025 were manually screened for urological relevance using FDA clearance documents (e.g., 510(k), De Novo) to confirm clinical application. Eligible devices were categorised by FDA approval pathway, review panel, indication, and year of approval. FDA decision summaries were reviewed to characterise study design, training/testing and external validation features, and reporting of patient demographics. Descriptive statistics were used to summarise distributions and temporal trends.

resultsOf 1358 FDA-cleared AI devices, 30 (2.2%) were urology specific. Approvals increased over time, with 26.7% cleared in 2025 (through September). The predominant FDA medical device category was Radiology (70.0%), with indications led by prostate imaging (43.3%). Information on study design, recruitment, and validation was frequently unavailable. Race and ethnicity were reported in one device (3.3%) for training/testing and four devices (13.3%) for external validation. Among these, aggregated proportions showed that the majority of subjects were White (86.1% for training/testing; 87.6% for external validation).

conclusionsThe FDA-authorised urological AI devices are dominated by radiology-based, prostate-focused applications and have increased in recent years. However, publicly available evidence supporting their authorisation is often limited, with incomplete reporting of study design and patient demographics. Greater transparency and post-authorisation evaluation are needed to support interpretation of these tools in clinical practice.

Indexed as

Artificial IntelligenceDevice ApprovalUnited States Food and Drug AdministrationUrologyHumansUnited Statesartificial intelligencedata reportingrepresentationUnited States Food and Drug Administrationurology

Identifiers

PMID42299919
PMCPMC13458661

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.