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The overall quality of the included studies was assessed using QUADAS\u2010AI tool. Fifty studies were included, of these 37\/50 (74.0%) on ovarian masses or ovarian cancer, 5\/50 (10.0%) on endometrial cancer, 5\/50 (10.0%) on cervical cancer, and 3\/50 (6.0%) on other malignancies. Most studies were at high risk of bias for\n                    <jats:italic>subject selection<\/jats:italic>\n                    (i.e., sample size, source, or scanner model were not specified; data were not derived from open\u2010source datasets; imaging preprocessing was not performed) and\n                    <jats:italic>index test<\/jats:italic>\n                    (AI models was not externally validated) and at low risk of bias for\n                    <jats:italic>reference standard<\/jats:italic>\n                    (i.e., the reference standard correctly classified the target condition) and\n                    <jats:italic>workflow<\/jats:italic>\n                    (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33\/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression\u2010free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.\n                  <\/jats:p>","DOI":"10.1002\/ijc.35092","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T07:10:46Z","timestamp":1720681846000},"page":"1832-1845","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review"],"prefix":"10.1002","volume":"155","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5070-7245","authenticated-orcid":false,"given":"Francesca","family":"Moro","sequence":"first","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna, del Bambino e di Sanit\u00e0 Pubblica Fondazione Policlinico Universitario Agostino Gemelli, IRCCS  Rome 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