{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:17:53Z","timestamp":1783387073488,"version":"3.54.6"},"reference-count":102,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics13182889","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"2889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-6734","authenticated-orcid":false,"given":"Benjamin M.","family":"Mervak","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Michigan\u2014Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica G.","family":"Fried","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan\u2014Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish P.","family":"Wasnik","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan\u2014Michigan Medicine, 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s41746-020-00333-z","article-title":"A Short Guide for Medical Professionals in the Era of Artificial Intelligence","volume":"3","author":"Mesko","year":"2020","journal-title":"NPJ Digit. 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