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
Affiliations (23)
Affiliations (23)
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy.
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- San Raffaele Vita-Salute University, Milan, Italy.
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK.
- Centre for Medical Imaging, University College London, London, UK.
- Military Institute of Medicine-National Research Institute, Warsaw, Poland.
- Affidea Poland, Warsaw, Poland.
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Institute of Health (BIH), Berlin, Germany.
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome/Policlinico Umberto I, Rome, Italy.
- Department of Imagery, Hôpital Pitié-Salpétrière, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Paris, France.
- Department of Urinary and Vascular Radiology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France.
- University Lyon 1, Lyon, France.
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
- Department of Clinical and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.
- Department of Diagnostic and Interventional Radiology, Tübingen University Hospital, Eberhard Karls University Tübingen, Tübingen, Germany.
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. [email protected].
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.