Evidence map›Paper›PMID 42142521›Full record

ArticleEuropean journal of radiology2026

Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.

Deependra Singh, Juan Pablo Salazar Gutiérrez, Olivier Rouviere, Francesco Giganti, Milagros Otero-García, Roderick C N van den Bergh, Monique J Roobol, Lionne D F Venderbos, Sarah Collen, Hendrik van Poppel and 3 more

Abstract readScoping Review
In one paragraph

Article in European journal of radiology, 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

13 authors.

Deependra SinghEarly Detection, Prevention, and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France. Electronic address: singhd@iarc.who.int.
Juan Pablo Salazar GutiérrezDepartment of Radiology, Althaia Foundation, Manresa, Spain.
Olivier RouviereDepartment of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; University Lyon 1, Lyon, France.
Francesco GigantiDepartment of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Division of Surgery & Interventional Science, University College London, London, UK.
Milagros Otero-GarcíaDepartment of Radiology, Hospital Universitario de Vigo, Spain.
Roderick C N van den BerghDepartment of Urology, Erasmus Cancer Institute, Erasmus University Medical Center Rotterdam, the Netherlands.
Monique J RoobolDepartment of Urology, Erasmus Cancer Institute, Erasmus University Medical Center Rotterdam, the Netherlands.
Lionne D F VenderbosDepartment of Urology, Erasmus Cancer Institute, Erasmus University Medical Center Rotterdam, the Netherlands.
Sarah CollenEuropean Association of Urology, Policy Office, Arnhem, the Netherlands.
Hendrik van PoppelEuropean Association of Urology, Policy Office, Arnhem, the Netherlands; Department of Urology, KU Leuven, Belgium.
Partha BasuEarly Detection, Prevention, and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France.
Arunah ChandranEarly Detection, Prevention, and Infections Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France.
PRAISE-U consortium members

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveArtificial Intelligence (AI) is seen as a potential solution to alleviate workforce demands arising from growing use of magnetic resonance imaging (MRI) in prostate cancer (PCa) screening. We aimed to synthesize the evidence on use of AI in prostate MRI readings in asymptomatic men in PCa screening settings.

methodsWe conducted a rapid scoping review following PRISMA-ScR guidelines and Cochrane rapid review methods performing the systematic search of major databases supplemented by grey literature search with no restrictions in study design and time-duration. We considered various aspects of utilization of AI in MRI interpretations and biopsy indications in the screening setting. Anticipating limited evidence on AI implementation in screening settings, we extended the review from 'what is known' to discussion on 'key considerations for expected expansion'. KEY FINDINGS AND LIMITATIONS: We identified 284 records with 47 studies assessed for eligibility and two studied met the inclusion criteria. Both evaluated commercially available ProstateAI software tool to interpret prostate MRI. Agreement between deep learning-based algorithm of AI and expert radiologist ranged from poor to moderate (kappa 0.17-0.42). AI demonstrated high tendency of over-detection and low specificity, leading to discordance with expert radiologists. CONCLUSIONS AND CLINICAL IMPLICATIONS: Current evidence on use of AI in prostate MRI interpretation is limited, but this review highlights several important directions for future research and implementation. Generating robust evidence base in the coming years will be crucial to ensure that AI integration enhances the effectiveness and acceptability of future prostate cancer screening programs. PATIENT SUMMARY: In this study, we examined whether Artificial Intelligence (AI) tools can accurately read prostate MRI scans of apparently healthy men for early detection of prostate cancer. We found that deploying current AI tools that are trained and tested in hospital referred patients may not be optimal to read MRI performed in asymptomatic men. We conclude that AI needs much more training and testing in real screening populations before it can be safely used in prostate cancer screening programs.

Indexed as

Artificial IntelligenceEarly Detection of CancerMagnetic Resonance ImagingProstatic NeoplasmsEvidence-Based MedicineHumansImage Interpretation, Computer-AssistedMaleReproducibility of ResultsSensitivity and SpecificityAIMRIProstate cancerRapid scoping reviewScreening

Identifiers

PMID42142521
PMCPMC13285858

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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.