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PubMed, Cochrane Library, and<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/ClinicalTrials.gov\">ClinicalTrials.gov<\/jats:ext-link>databases were searched using the following keywords \u201cneural networks\u201d, OR \u201cartificial intelligence\u201d, OR \u201cmachine learning\u201d, OR \u201cdeep learning\u201d, AND \u201cobstetrics\u201d, OR \u201cobstetrical\u201d, OR \u201cfetus\u201d, OR \u201cfoetus\u201d, OR \u201cfetal\u201d, OR \u201cfoetal\u201d, OR \u201cpregnancy\u201d, or \u201cpregnant\u201d, AND \u201cultrasound\u201d from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors\u2019 search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.<\/jats:p>","DOI":"10.1002\/pd.6411","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T10:36:12Z","timestamp":1690540572000},"page":"1176-1219","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Artificial intelligence in obstetric ultrasound: A scoping review"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2752-4061","authenticated-orcid":false,"given":"Rebecca","family":"Horgan","sequence":"first","affiliation":[{"name":"Division of Maternal Fetal Medicine Department of Obstetrics &amp; Gynecology Eastern Virginia Medical School Norfolk Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lea","family":"Nehme","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine Department of Obstetrics &amp; Gynecology Eastern Virginia Medical School Norfolk Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred","family":"Abuhamad","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine Department of Obstetrics &amp; Gynecology Eastern Virginia Medical School Norfolk Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Artificial intelligence technologies. 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