Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.

Authors

Renato Cuocolo, Andrea Ponsiglione, Georgios Agrotis, Tristan Barrett, Giorgio Brembilla, Iztok Caglic, Hanna Falińska, Charlie Alexander Hamm, Emanuele Messina, Tobias Penzkofer, Raphaële Renard-Penna, Olivier Rouvière, Luca Russo, Evis Sala, Johannes Uhlig, Anwar R Padhani, Maarten de Rooij, ESUR Prostate MRI Working Group

Published in

European radiology. Sep 18, 2026. Epub Sep 18, 2026.

Abstract

This report from the ESUR Prostate MRI Working Group assesses the current role of artificial intelligence (AI) in MRI-based detection of prostate cancer. While deep learning tools demonstrate high technical accuracy, the report emphasizes a significant gap between research achievements and actual clinical application. Key statements include promoting a "human-in-the-loop" approach, where AI supports rather than replaces radiological expertise, to reduce risks such as automation bias and legal liability. There is an urgent need for prospective validation across multiple vendors and for implementing post-market surveillance to monitor algorithmic drift. Finally, the group identified research priorities focused on cost-effectiveness, transparency via explainable AI, and addressing the unique challenges of deploying these tools in population-based screening programs. KEY POINTS: Question What challenges hinder the clinical adoption of AI-based medical devices for prostate cancer detection? Findings Major obstacles include insufficient real-world validation, complex dynamics of human-AI interactions that require a human-in-the-loop approach, and the need for ongoing post-market surveillance for oncologic safety. Clinical relevance This report highlights the gap between AI's research promise and clinical readiness. It underscores the need for localvalidation, post-market surveillance, and adequate user training as AI tools become incorporated into diagnostic prostate MRI workflows.

PMID:
42760428
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.

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