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The goal of the AI was to detect clinically significant prostate cancer (csPC), defined as International Society of Urological Pathology (ISUP) grade 2 or higher. The AI system was compared to the performance of radiologists using PI-RADS v2 evaluation metrics. Histopathology was used as the reference standard in the dataset. To better verify negative cases, 288 men were subject to systematic biopsies regardless of MRI findings, and all men had at least 3 years of follow-up.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1354 MRI examinations in 1254 men with a median age of 58 years (range 50\u201363 years) were randomly divided into a training set (1086 examinations) and a test set (268 examinations). The resulting area under the receiver operating characteristic curve (AUROC) was 0.83 (95% CI 0.73\u20130.92) for the AI system; however, with significantly lower specificity at matched sensitivity levels compared to radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A prostate MRI dataset from a screening population with histological confirmation was curated and evaluated with AI. The neural network trained and tested on this data produced lower specificities than the radiologists.<\/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>Does an AI system trained in a screening cohort perform as well as radiologists?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>An AI trained on screening data achieved an AUROC of 0.83 (95% CI 0.73\u20130.92) with lower specificity at the same sensitivity levels as radiologists.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>An AI system trained in a screening population has lower specificity than radiologists using PI-RADS v2.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-12198-5","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T12:31:23Z","timestamp":1765197083000},"page":"3418-3428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8706-4036","authenticated-orcid":false,"given":"Fredrik","family":"Langkilde","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magnus","family":"Gren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Wallstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Kuczera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan E.","family":"Maier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"12198_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.eururo.2020.09.042","volume":"79","author":"N Mottet","year":"2021","unstructured":"Mottet N, van den Bergh RCN, Briers E et al (2021) EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer\u20142020 update. 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