ArticleEuropean radiology experimental2026
Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?
Article in European radiology experimental, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Editorial for "AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers".Journal of magnetic resonance imaging : JMRI · 2026Article
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Diagnostic performance of bpMRI versus mpMRI and AI-assisted bpMRI in prostate cancer detection: a multi-reader study.La Radiologia medica · 2026Article
- Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.Abdominal radiology (New York) · 2026Review
- The evolving PI-RADS paradigm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe evaluated a commercial artificial intelligence (AI) system as a concurrent decision-support tool for clinically significant prostate cancer (csPCa) detection. MATERIALS AND
methodsIn our retrospective study, consecutive patients underwent multiparametric MRI for clinical suspicion of PCa. All scans were reviewed by six readers with varying expertise (two expert radiologists, > 1,000 cases; two basic radiologists, 400‒1,000 cases; and two residents), with and without AI assistance. Intra-/inter-reader agreements and the impact of AI-assistance on patient-level csPCa scores and diagnostic performance, as well as benefit-to-harm ratios, were assessed.
resultsThe population consisted of 100 patients with a 26% prevalence of csPCa. There was no improvement in inter-reader agreement with AI-assistance versus without (Fleiss κ 0.573 and 0.584, respectively). Residents were most likely to change PI-RADS scores on AI-assisted readings compared to basic and expert radiologists (19, 9, and 7 changes, respectively). Overall, there was no significant difference in area under the receiving operating characteristic curve between AI-assisted and AI-unassisted readings (0.87 versus 0.86; p = 0.734). At a PI-RADS ≥ 3 threshold, sensitivity was slightly lower with AI (0.87 versus 0.89), while specificity (0.73), positive predictive value (0.53-0.54), and negative predictive value (0.94-0.95) remained similar. Subgroup analyses showed no significant differences in diagnostic performance. A slight increase in grade selectivity and selective biopsy avoidance rate was observed among experts and residents, respectively, with AI-assisted readings when applying a PI-RADS cutoff of 3 or PSA density ≥ 0.15 ng/mL/mL.
conclusionsAI did not significantly improve diagnostic accuracy across readers of varying expertise, with minor impacts on benefit-to-harm ratios. RELEVANCE STATEMENT: We found that AI support in prostate MRI did not significantly improve diagnostic accuracy across readers of varying experience, highlighting the need for further research to optimize AI integration and define its most clinically meaningful roles in prostate cancer detection. KEY POINTS: Residents were most prone to PI-RADS score modifications after AI-assisted readings compared to AI-unassisted and expert readers. There was no significant difference in diagnostic performance metrics between AI-assisted and unassisted readings. A slight improvement in grade selectivity among experts and in selective biopsy avoidance among residents was observed during AI-assisted readings for biopsy recommendations.
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