{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:07:01Z","timestamp":1789754821142,"version":"4.0.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>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 \u201chuman-in-the-loop\u201d 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>What challenges hinder the clinical adoption of AI-based medical devices for prostate cancer detection?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>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<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>This report highlights the gap between AI\u2019s 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<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12836-6","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:26:32Z","timestamp":1789752392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors"],"prefix":"10.1007","author":[{"given":"Renato","family":"Cuocolo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Ponsiglione","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Agrotis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Barrett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Brembilla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iztok","family":"Caglic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Fali\u0144ska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlie Alexander","family":"Hamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Messina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Penzkofer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rapha\u00eble","family":"Renard-Penna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Rouvi\u00e8re","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Russo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evis","family":"Sala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Uhlig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anwar R.","family":"Padhani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7257-7907","authenticated-orcid":false,"given":"Maarten","family":"de Rooij","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"on behalf of the ESUR Prostate MRI Working Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"12836_CR1","doi-asserted-by":"publisher","unstructured":"Johnson PM, Umapathy L, Gigax B et al (2025) Artificial intelligence in prostate MRI: addressing current limitations through emerging technologies. J Magn Reson Imaging. https:\/\/doi.org\/10.1002\/jmri.70189","DOI":"10.1002\/jmri.70189"},{"key":"12836_CR2","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","volume":"25","author":"A Saha","year":"2024","unstructured":"Saha A, Bosma JS, Twilt JJ et al (2024) Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 25:879\u2013887","journal-title":"Lancet Oncol"},{"key":"12836_CR3","doi-asserted-by":"publisher","unstructured":"Wu H, Liu F, Yang Q et al (2025) Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-025-66593-z","DOI":"10.1038\/s41467-025-66593-z"},{"key":"12836_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111017","volume":"166","author":"KL Lee","year":"2023","unstructured":"Lee KL, Kessler DA, Dezonie S et al (2023) Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality. Eur J Radiol 166:111017","journal-title":"Eur J Radiol"},{"key":"12836_CR5","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230138","volume":"6","author":"MK Fassia","year":"2024","unstructured":"Fassia MK, Balasubramanian A, Woo S et al (2024) Deep learning prostate MRI segmentation accuracy and robustness: a systematic review. Radiol Artif Intell 6:e230138","journal-title":"Radiol Artif Intell"},{"key":"12836_CR6","doi-asserted-by":"publisher","first-page":"6229","DOI":"10.1007\/s00330-024-10700-z","volume":"34","author":"CA Hamm","year":"2024","unstructured":"Hamm CA, Baumgartner GL, Padhani AR et al (2024) Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur Radiol 34:6229\u20136240","journal-title":"Eur Radiol"},{"key":"12836_CR7","doi-asserted-by":"publisher","first-page":"188","DOI":"10.2214\/AJR.21.26917","volume":"219","author":"B Turkbey","year":"2022","unstructured":"Turkbey B, Haider MA (2022) Artificial intelligence for automated cancer detection on prostate MRI: opportunities and ongoing challenges, from the AJR special series on AI applications. AJR Am J Roentgenol 219:188\u2013194","journal-title":"AJR Am J Roentgenol"},{"key":"12836_CR8","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/s00330-025-11830-8","volume":"36","author":"N Antonissen","year":"2026","unstructured":"Antonissen N, Tryfonos O, Houben IB, Jacobs C, de Rooij M, van Leeuwen KG (2026) Artificial intelligence in radiology: 173 commercially available products and their scientific evidence. Eur Radiol 36:526\u2013536","journal-title":"Eur Radiol"},{"key":"12836_CR9","doi-asserted-by":"publisher","first-page":"4915","DOI":"10.1007\/s00330-024-11323-0","volume":"35","author":"F Giganti","year":"2025","unstructured":"Giganti F, Moreira da Silva N, Yeung M et al (2025) AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study. Eur Radiol 35:4915\u20134924","journal-title":"Eur Radiol"},{"key":"12836_CR10","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.euf.2024.04.007","volume":"10","author":"K Guenzel","year":"2024","unstructured":"Guenzel K, Lukas Baumgaertner G, Padhani AR et al (2024) Diagnostic utility of artificial intelligence-assisted transperineal biopsy planning in prostate cancer suspected men: a prospective cohort study. Eur Urol Focus 10:833\u2013842","journal-title":"Eur Urol Focus"},{"key":"12836_CR11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232788","volume":"314","author":"YJ Lee","year":"2025","unstructured":"Lee YJ, Moon HW, Choi MH et al (2025) MRI-based deep learning algorithm for assisting clinically significant prostate cancer detection: a bicenter prospective study. Radiology 314:e232788","journal-title":"Radiology"},{"key":"12836_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.eururo.2025.04.018","volume":"88","author":"AB Ng","year":"2025","unstructured":"Ng AB, Giganti F, Kasivisvanathan V (2025) Artificial intelligence in prostate cancer diagnosis on magnetic resonance imaging: time for a new PARADIGM. Eur Urol 88:4\u20137","journal-title":"Eur Urol"},{"key":"12836_CR13","doi-asserted-by":"crossref","unstructured":"Rosesetti S (2025) The court of justice of the European Union Confirms the Existence of the Right to Explanation of Automated Decision-Making. European Law Blog","DOI":"10.21428\/9885764c.f1cc5989"},{"key":"12836_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105689","volume":"193","author":"Y Abas Mohamed","year":"2025","unstructured":"Abas Mohamed Y, Ee Khoo B, Shahrimie Mohd Asaari M, Ezane Aziz M, Rahiman Ghazali F (2025) Decoding the black box: explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning\u2014a state-of-the art systematic review. Int J Med Inf 193:105689","journal-title":"Int J Med Inf"},{"key":"12836_CR15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222276","volume":"307","author":"CA Hamm","year":"2023","unstructured":"Hamm CA, Baumgartner GL, Biessmann F et al (2023) Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology 307:e222276","journal-title":"Radiology"},{"key":"12836_CR16","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13244-025-01905-x","volume":"16","author":"E Kotter","year":"2025","unstructured":"Kotter E, D\u2019Antonoli TA, Cuocolo R et al (2025) Guiding AI in radiology: ESR\u2019s recommendations for effective implementation of the European AI Act. Insights Imaging 16:33","journal-title":"Insights Imaging"},{"key":"12836_CR17","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1186\/s13244-025-02146-8","volume":"16","author":"R Cuocolo","year":"2025","unstructured":"Cuocolo R, Bernardini D, Pinto Dos Santos D et al (2025) AI medical device post-market surveillance regulations: consensus recommendations by the European Society of Radiology. Insights Imaging 16:275","journal-title":"Insights Imaging"},{"key":"12836_CR18","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1186\/s13244-024-01801-w","volume":"15","author":"M Zanardo","year":"2024","unstructured":"Zanardo M, Visser JJ, Colarieti A et al (2024) Impact of AI on radiology: a EuroAIM\/EuSoMII 2024 survey among members of the European Society of Radiology. Insights Imaging 15:240","journal-title":"Insights Imaging"},{"key":"12836_CR19","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220146","volume":"5","author":"CF de Vries","year":"2023","unstructured":"de Vries CF, Colosimo SJ, Staff RT et al (2023) Impact of different mammography systems on artificial intelligence performance in breast cancer screening. Radiol Artif Intell 5:e220146","journal-title":"Radiol Artif Intell"},{"key":"12836_CR20","doi-asserted-by":"publisher","unstructured":"Klontzas ME, Groot Lipman KBW, Akinci D\u2019 Antonoli T et al (2025) ESR essentials: common performance metrics in AI-practice recommendations by the European Society of Medical Imaging Informatics. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-025-11890-w","DOI":"10.1007\/s00330-025-11890-w"},{"key":"12836_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrai.2025.100030","volume":"3","author":"B Kocak","year":"2025","unstructured":"Kocak B, Klontzas ME, Stanzione A et al (2025) Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations. Eur J Radiol Artif Intell 3:100030","journal-title":"Eur J Radiol Artif Intell"},{"key":"12836_CR22","doi-asserted-by":"publisher","first-page":"4012","DOI":"10.1007\/s00261-020-02547-x","volume":"45","author":"IG Schoots","year":"2020","unstructured":"Schoots IG, Padhani AR (2020) Delivering clinical impacts of the MRI diagnostic pathway in prostate cancer diagnosis. Abdom Radiol (NY) 45:4012\u20134022","journal-title":"Abdom Radiol (NY)"},{"key":"12836_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111156","volume":"169","author":"A Ponsiglione","year":"2023","unstructured":"Ponsiglione A, Cuocolo R (2023) Radiology under siege? Adversarial attacks against deep learning algorithms. Eur J Radiol 169:111156","journal-title":"Eur J Radiol"},{"key":"12836_CR24","doi-asserted-by":"publisher","first-page":"5695","DOI":"10.1007\/s00330-025-11498-0","volume":"35","author":"AR Padhani","year":"2025","unstructured":"Padhani AR, Papanikolaou N (2025) AI and human interactions in prostate cancer diagnosis using MRI. Eur Radiol 35:5695\u20135700","journal-title":"Eur Radiol"},{"key":"12836_CR25","doi-asserted-by":"publisher","unstructured":"Cuocolo R, Huisman M (2026) The augmentation myth: AI, economics, and workforce substitution in radiology. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-026-12568-7","DOI":"10.1007\/s00330-026-12568-7"},{"key":"12836_CR26","doi-asserted-by":"publisher","first-page":"e2515672","DOI":"10.1001\/jamanetworkopen.2025.15672","volume":"8","author":"JJ Twilt","year":"2025","unstructured":"Twilt JJ, Saha A, Bosma JS et al (2025) AI-assisted vs unassisted identification of prostate cancer in magnetic resonance images. JAMA Netw Open 8:e2515672","journal-title":"JAMA Netw Open"},{"key":"12836_CR27","doi-asserted-by":"publisher","unstructured":"Xie XR, Hou Y, Shan S et al (2025) AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis. Prostate Cancer Prostatic Dis. https:\/\/doi.org\/10.1038\/s41391-025-01053-9","DOI":"10.1038\/s41391-025-01053-9"},{"key":"12836_CR28","doi-asserted-by":"publisher","first-page":"2234","DOI":"10.1002\/jmri.29660","volume":"61","author":"Z Sun","year":"2025","unstructured":"Sun Z, Wang K, Gao G et al (2025) Assessing the performance of artificial intelligence assistance for prostate MRI: a two-center study involving radiologists with different experience levels. J Magn Reson imaging 61:2234\u20132245","journal-title":"J Magn Reson imaging"},{"key":"12836_CR29","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1016\/j.euo.2024.03.003","volume":"7","author":"T Couchoux","year":"2024","unstructured":"Couchoux T, Jaouen T, Melodelima-Gonindard C et al (2024) Performance of a region of interest-based algorithm in Diagnosing International Society of Urological Pathology Grade Group \u2265 2 Prostate Cancer on the MRI-FIRST Database-CAD-FIRST Study. Eur Urol Oncol 7:1113\u20131122","journal-title":"Eur Urol Oncol"},{"key":"12836_CR30","doi-asserted-by":"publisher","unstructured":"Gelikman DG, Yilmaz EC, Harmon SA et al (2025) Evaluating artificial intelligence-assisted prostate biparametric MRI interpretation: an international multireader study. AJR Am J Roentgenol. https:\/\/doi.org\/10.2214\/AJR.24.32399:1-11","DOI":"10.2214\/AJR.24.32399:1-11"},{"key":"12836_CR31","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1007\/s00330-025-11727-6","volume":"35","author":"SJ Fransen","year":"2025","unstructured":"Fransen SJ, Bosma JS, van Lohuizen Q et al (2025) Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric. Eur Radiol 35:7821\u20137831","journal-title":"Eur Radiol"},{"key":"12836_CR32","doi-asserted-by":"publisher","first-page":"e507","DOI":"10.1016\/S2589-7500(22)00070-X","volume":"4","author":"C Leibig","year":"2022","unstructured":"Leibig C, Brehmer M, Bunk S, Byng D, Pinker K, Umutlu L (2022) Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health 4:e507\u2013e519","journal-title":"Lancet Digit Health"},{"key":"12836_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-51725-8","volume":"15","author":"HML Frazer","year":"2024","unstructured":"Frazer HML, Pena-Solorzano CA, Kwok CF et al (2024) Comparison of AI-integrated pathways with human\u2013AI interaction in population mammographic screening for breast cancer. Nat Commun 15:7525","journal-title":"Nat Commun"},{"key":"12836_CR34","doi-asserted-by":"publisher","unstructured":"Langkilde F, Gren M, Wallstrom J, Kuczera S, Maier SE (2025) Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-025-12198-5","DOI":"10.1007\/s00330-025-12198-5"},{"key":"12836_CR35","first-page":"75","volume":"31","author":"B Kocak","year":"2025","unstructured":"Kocak B, Ponsiglione A, Stanzione A et al (2025) Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects. Diagn Interv Radiol 31:75\u201388","journal-title":"Diagn Interv Radiol"},{"key":"12836_CR36","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.243688","volume":"316","author":"JJJ Gommers","year":"2025","unstructured":"Gommers JJJ, Verboom SD, Duvivier KM et al (2025) Influence of AI decision support on radiologists\u2019 performance and visual search in screening mammography. Radiology 316:e243688","journal-title":"Radiology"},{"key":"12836_CR37","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1097\/HMR.0b013e31821826a1","volume":"36","author":"T Hoff","year":"2011","unstructured":"Hoff T (2011) Deskilling and adaptation among primary care physicians using two work innovations. Health Care Manag Rev 36:338\u2013348","journal-title":"Health Care Manag Rev"},{"key":"12836_CR38","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13244-024-01893-4","volume":"16","author":"M Savardi","year":"2025","unstructured":"Savardi M, Signoroni A, Benini S et al (2025) Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic. Insights Imaging 16:23","journal-title":"Insights Imaging"},{"key":"12836_CR39","doi-asserted-by":"publisher","unstructured":"Winkel DJ, Tong A, Lou B et al (2021) A novel deep learning based computer-aided diagnosis system improves the accuracy and efficiency of radiologists in reading biparametric magnetic resonance images of the prostate: results of a multireader, multicase study. Invest Radiol. https:\/\/doi.org\/10.1097\/rli.0000000000000780","DOI":"10.1097\/rli.0000000000000780"},{"key":"12836_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111252","volume":"170","author":"K Wenderott","year":"2024","unstructured":"Wenderott K, Krups J, Luetkens JA, Gambashidze N, Weigl M (2024) Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists\u2019 outcomes. Eur J Radiol 170:111252","journal-title":"Eur J Radiol"},{"key":"12836_CR41","doi-asserted-by":"publisher","unstructured":"Raya-Povedano JL, Romero-Mart\u00edn S, El\u00edas-Cabot E, Gubern-M\u00e9rida A, Rodr\u00edguez-Ruiz A, \u00c1lvarez-Benito M (2021) AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation. Radiology. https:\/\/doi.org\/10.1148\/radiol.2021203555:203555","DOI":"10.1148\/radiol.2021203555:203555"},{"key":"12836_CR42","doi-asserted-by":"publisher","unstructured":"Schiaffino S, Girometti R (2025) Artificial intelligence in breast imaging: From theoretical promise to clinical mirage? Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-025-11828-2","DOI":"10.1007\/s00330-025-11828-2"},{"key":"12836_CR43","doi-asserted-by":"publisher","first-page":"6168","DOI":"10.1007\/s00330-024-10626-6","volume":"34","author":"AR Padhani","year":"2024","unstructured":"Padhani AR, Godtman RA, Schoots IG (2024) Key learning on the promise and limitations of MRI in prostate cancer screening. Eur Radiol 34:6168\u20136174","journal-title":"Eur Radiol"},{"key":"12836_CR44","doi-asserted-by":"publisher","first-page":"4908","DOI":"10.1007\/s00330-020-07545-7","volume":"31","author":"V Bura","year":"2021","unstructured":"Bura V, Caglic I, Snoj Z et al (2021) MRI features of the normal prostatic peripheral zone: the relationship between age and signal heterogeneity on T2WI, DWI, and DCE sequences. Eur Radiol 31:4908\u20134917","journal-title":"Eur Radiol"},{"key":"12836_CR45","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1148\/radiol.204097","volume":"303","author":"T Ueda","year":"2022","unstructured":"Ueda T, Ohno Y, Yamamoto K et al (2022) Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology 303:373\u2013381","journal-title":"Radiology"},{"key":"12836_CR46","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1002\/jmri.27879","volume":"55","author":"S Cipollari","year":"2022","unstructured":"Cipollari S, Guarrasi V, Pecoraro M et al (2022) Convolutional neural networks for automated classification of prostate multiparametric magnetic resonance imaging based on image quality. J Magn Reson Imaging 55:480\u2013490","journal-title":"J Magn Reson Imaging"},{"key":"12836_CR47","doi-asserted-by":"publisher","first-page":"e200024","DOI":"10.1148\/rycan.2021200024","volume":"3","author":"M Bardis","year":"2021","unstructured":"Bardis M, Houshyar R, Chantaduly C et al (2021) Segmentation of the prostate transition zone and peripheral zone on MR images with deep learning. Radio Imaging Cancer 3:e200024","journal-title":"Radio Imaging Cancer"},{"key":"12836_CR48","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.diii.2022.11.005","volume":"104","author":"O Rouviere","year":"2023","unstructured":"Rouviere O, Jaouen T, Baseilhac P et al (2023) Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts?\u2014a systematic review. Diagn Interv Imaging 104:221\u2013234","journal-title":"Diagn Interv Imaging"},{"key":"12836_CR49","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/s00261-018-1712-z","volume":"44","author":"R Campa","year":"2019","unstructured":"Campa R, Del Monte M, Barchetti G et al (2019) Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy. Abdom Radiol (NY) 44:264\u2013271","journal-title":"Abdom Radiol (NY)"},{"key":"12836_CR50","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-025-01602-7","volume":"25","author":"Z Wang","year":"2025","unstructured":"Wang Z, Lu P, Liu S et al (2025) Comparison of the impact of rectal susceptibility artifacts in prostate magnetic resonance imaging on subjective evaluation and deep learning: a two-center retrospective study. BMC Med Imaging 25:61","journal-title":"BMC Med Imaging"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12836-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12836-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12836-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:26:34Z","timestamp":1789752394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12836-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":50,"alternative-id":["12836"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12836-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]},"assertion":[{"value":"16 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The scientific guarantor of this publication is Maarten de Rooij.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"A.P. is a Junior Deputy Editor in European Radiology, and as such has not taken part in the review or selection process of this article. R.C. serves as an Editorial Board Member of European Radiology; he has not taken part in the review or selection process of this article. E.S. is a co-founder of Lucida Medical Ltd. L.R. is a consultant for Lucida Medical Ltd and receives speaker honoraria for GE Healthcare. O.R. is a patent holder in the field of AI (international patent WO2022229265AI0). M.d.R. receives speaker honoraria for talks from Siemens Healthineers. A.R.P. is the Chief Medical Officer of Lucida Medical Ltd, is a member of the PI-CAI Scientific board, and has advised Siemens Healthineers on their Prostate AI developments\n                      .","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}