Evidence map›Paper›PMID 41844851›Full record

SynthesisEuropean radiology2026

Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.

Marco Antonio Andrade, Henrique Rodrigues, Caio Hernandes Colhado, Nathan Joseph Silva Godinho, Rhuan Dorigueto Dos Santos, Alexandre Leite de Andrade, Taylor Goodstein

Abstract readMeta-AnalysisSystematic ReviewComparative Study
PubMed Publisher
In one paragraph

Synthesis in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Marco Antonio AndradeSchool of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil. marcojuniorapple@gmail.com.ORCID http://orcid.org/0009-0005-3379-2176
Henrique RodriguesSchool of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Caio Hernandes ColhadoSchool of Medicine, School of Medicine of Sao Jose do Rio Preto, Sao Jose do Rio Preto, Brazil.
Nathan Joseph Silva GodinhoSchool of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Rhuan Dorigueto Dos SantosSchool of Engineering, Federal Institute of Espirito Santo (IFES), Linhares, Brazil.
Alexandre Leite de AndradeSchool of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.
Taylor GoodsteinDepartment of Urology, Emory University, Atlanta, GA, 30322, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMultiparametric magnetic resonance imaging (mpMRI) detects clinically significant prostate cancer (csPCa, Gleason Grade Group ≥ 2) with high sensitivity but limited specificity and inter-reader variability. Artificial intelligence (AI), particularly convolutional neural networks (CNNs) and deep learning, may improve diagnostic consistency and accuracy. This meta-analysis compares AI systems and experienced radiologists in detecting csPCa using mpMRI.

methodsWe performed a systematic review and meta-analysis of English-language non-RCT studies. PubMed, Embase, and Cochrane databases were searched up to May 2025, yielding 855 studies. Only studies comparing CNN-based or deep-learning AI models to radiologists were included. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating curve (AUC) were calculated using a bivariate random-effects model.

resultsTen studies with 2586 patients were analyzed. AI systems showed pooled sensitivity of 0.90 (95% CI 0.84-0.94) and specificity of 0.69 (95% CI 0.45-0.85). Radiologists had a sensitivity of 0.89 (95% CI 0.82-0.94) and a specificity of 0.60 (95% CI 0.43-0.75). DOR was 17.54 (95% CI 9.34-32.94) for AI and 12.35 (95% CI 4.96-30.76) for radiologists. Summary receiver operating characteristic (SROC) curves indicated similar diagnostic accuracy, with AI slightly outperforming radiologists (AUC 0.88 vs. 0.85).

conclusionAI systems perform comparably to radiologists in detecting csPCa on mpMRI, with a potential edge in specificity, though confidence intervals overlapped. High heterogeneity and the retrospective nature of all included studies limit reliability, necessitating prospective validation. AI could serve as an adjunct in prostate cancer diagnosis, potentially improving precision and reducing unnecessary biopsies with further model refinement. KEY POINTS: Question Interpretation of mpMRI for clinically significant prostate cancer varies among radiologists, affecting diagnostic consistency. Findings Meta-analysis shows AI has comparable sensitivity and potentially superior specificity to radiologists in detecting significant prostate cancer on mpMRI, though confidence intervals overlapped. Clinical relevance AI has the potential to enhance diagnostic accuracy, reduce unnecessary biopsies, and improve consistency in prostate cancer detection, thereby supporting more reliable and standardized imaging assessments across centers.

Indexed as

Artificial IntelligenceMultiparametric Magnetic Resonance ImagingProstatic NeoplasmsRadiologistsConvolutional Neural NetworksDeep LearningHumansMaleReproducibility of ResultsSensitivity and SpecificityArtificial intelligenceDiagnostic accuracyMultiparametric MRIProstate cancer

Identifiers

PMID41844851

What OpenQuestion holds

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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.