{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:06:14Z","timestamp":1790971574097,"version":"4.1.0"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"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) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed\/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI\u2019s role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight.<\/jats:p>","DOI":"10.3390\/diagnostics15070866","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T13:36:49Z","timestamp":1743169009000},"page":"866","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1715-0768","authenticated-orcid":false,"given":"Ivan","family":"Male\u0161","sequence":"first","affiliation":[{"name":"Department of Abdominal Surgery, University Hospital of Split, Spin\u010di\u0107eva 1, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9696-3359","authenticated-orcid":false,"given":"Marko","family":"Kumri\u0107","sequence":"additional","affiliation":[{"name":"Department of Pathophysiology, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"},{"name":"Laboratory for Cardiometabolic Research, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Hui\u0107 Male\u0161","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University Hospital of Split, Spin\u010di\u0107eva 1, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Cvitkovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Intensive Care, University Hospital of Split, Spin\u010di\u0107eva 1, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roko","family":"\u0160anti\u0107","sequence":"additional","affiliation":[{"name":"Department of Pathophysiology, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1517-720X","authenticated-orcid":false,"given":"Zenon","family":"Pogoreli\u0107","sequence":"additional","affiliation":[{"name":"Department of Surgery, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"},{"name":"Department of Pediatric Surgery, University Hospital of Split, Spin\u010di\u0107eva 1, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1634-0635","authenticated-orcid":false,"given":"Jo\u0161ko","family":"Bo\u017ei\u0107","sequence":"additional","affiliation":[{"name":"Department of Pathophysiology, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"},{"name":"Laboratory for Cardiometabolic Research, School of Medicine, University of Split, \u0160oltanska 2A, 21000 Split, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.2174\/1389202922666210705124359","article-title":"Machine Learning in Healthcare","volume":"22","author":"Habehh","year":"2021","journal-title":"Curr. 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