{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T11:56:28Z","timestamp":1793620588766,"version":"4.5.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>In today\u2019s medical practice clinicians need to struggle with a huge amount of data to improve the outcomes of the patients. Sometimes one clinician needs to deal with thousands of ultrasound images or hundred papers of laboratory results. To overcome this shortage, computers get in help of human beings and they are educated under the term \u201cartificial intelligence.\u201d We were using artificial intelligence in our daily lives (i.e., Google, Netflix, etc.), but applications in medicine are relatively new. In obstetrics and gynecology, artificial intelligence models mostly use ultrasound images for diagnostic purposes but nowadays researchers started to use other medical recordings like non-stress tests or urodynamics study results to develop artificial intelligence applications. Urogynecology is a developing subspecialty of obstetrics and gynecology, and articles about artificial intelligence in urogynecology are limited but in this review, we aimed to increase clinicians\u2019 knowledge about this new approach.<\/jats:p>","DOI":"10.3389\/fmed.2023.1098205","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:59:35Z","timestamp":1677113975000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Current developments in artificial intelligence from obstetrics and gynecology to urogynecology"],"prefix":"10.3389","volume":"10","author":[{"given":"Mehmet Murat","family":"Seval","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bulut","family":"Varl\u0131","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1046\/j.1469-0705.1999.13010017.x","article-title":"Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses","volume":"13","author":"Timmerman","year":"1999","journal-title":"Ultrasound Obstet 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