biostudies-literatureBogaerts JMKWF Kankerbestrijdinge70006https://www.ebi.ac.uk/biostudies/studies/S-EPMC11496567biostudies-literatureUnknown10(6)In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade 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&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessioThe journal of pathology. Clinical researchAssessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.PMC1149656712950Schwartz LEBrinkhuis MKooreman LRabban JTBogaerts JMvan Zanten MDevouassoux-Shisheboran MSteenbeek MPMartin SMilla JFernandez-Perez JKleijn TGBentz JLStaebler ARijstenberg LGuerriero ALastra RRde Hullu JANtala CVolchek MFischer ASchoolmeester JKJaconi MParkash VVang Rde Pauw CNuman TAAddante FZannoni GFTurashvili GSoong TRCordier FAbete LVan de Vijver KBokhorst JMAI‐STIC Study GroupAliredjo RPBosse TShih IMBart JGilks CBvan der Laak JABulten JNarducci NKusters-Vandevelde HSimons MRottscholl Rvan Bommel MHDesouki MMChrzan AfalseAssessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade 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&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessio2024-01-01T00:00:00Z2024 Nov2025-04-04T02:21:28.873Z2025-04-04T02:21:28.873ZS-EPMC114965673943921310.1002/2056-4538.70006