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This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n\u2009=\u200916, 28%), gynecology (n\u2009=\u200915, 26%), hepatobiliary system (n\u2009=\u200913, 23%), and musculoskeletal (n\u2009=\u20098, 14%). The main methodological applications included deep learning (n\u2009=\u200937, 65%), machine learning (n\u2009=\u200913, 23%), natural language processing (n\u2009=\u20095, 9%), and robots (n\u2009=\u20092, 4%). The majority of the studies were single-center (n\u2009=\u200943, 75%) and retrospective (n\u2009=\u200956, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal\/pelvic imaging (infertility monitoring and follicle development).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The application of AI in abdominal\/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00261-024-04640-x","type":"journal-article","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:03:23Z","timestamp":1730523803000},"page":"1775-1789","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications"],"prefix":"10.1007","volume":"50","author":[{"given":"Lie","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Pfob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"4640_CR1","doi-asserted-by":"publisher","first-page":"4581","DOI":"10.1038\/s41467-022-32186-3","volume":"13","author":"MA Ricci Lara","year":"2022","unstructured":"Ricci Lara MA, Echeveste R, Ferrante E (2022) Addressing fairness in artificial intelligence for medical imaging. 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