{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T20:12:16Z","timestamp":1786565536043,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["12950"],"award-info":[{"award-number":["12950"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["pathsocjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Pathology CR"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology\u2010related tasks. An example is our deep\u2010learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high\u2010grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&amp;E\u2010stained slides of fallopian tubes (30 cases\/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep\u2010learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed\u2010models analysis. With AI assistance, we found a significant increase in accuracy (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44\u2009s (32%) reduction in slide review time (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10\u2010point scale (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.<\/jats:p>","DOI":"10.1002\/2056-4538.70006","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T02:19:54Z","timestamp":1729649994000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Assessing the impact of deep\u2010learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (<scp>STIC<\/scp>) in fallopian tubes"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0772-6712","authenticated-orcid":false,"given":"Joep MA","family":"Bogaerts","sequence":"first","affiliation":[{"name":"Department of Pathology Radboud University Medical Center  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miranda 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