{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:16:21Z","timestamp":1791540981373,"version":"4.3.4"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T00:00:00Z","timestamp":1743206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational tools for risk stratification but often lack early precision. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in AP management, focusing on their applications in diagnosis, severity prediction, complication management, and treatment optimization. A comprehensive analysis of recent studies was conducted, highlighting ML models such as XGBoost, neural networks, and multimodal approaches. These models integrate clinical, laboratory, and imaging data, including radiomics features, and are useful in diagnostic and prognostic accuracy in AP. Special attention was given to models addressing SAP, complications like acute kidney injury and acute respiratory distress syndrome, mortality, and recurrence. AI-based models achieved higher AUC values than traditional models in predicting acute pancreatitis outcomes. XGBoost reached an AUC of 0.93 for early SAP prediction, higher than BISAP (AUC 0.74) and APACHE II (AUC 0.81). PrismSAP, integrating multimodal data, achieved the highest AUC of 0.916. AI models also demonstrated superior accuracy in mortality prediction (AUC 0.975) and ARDS detection (AUC 0.891) AI and ML represent a transformative advance in AP management, facilitating personalized treatment, early risk stratification, and allowing resource utilization to be optimized. By addressing challenges such as model generalizability, ethical considerations, and clinical adoption, AI has the potential to significantly improve patient outcomes and redefine AP care standards globally.<\/jats:p>","DOI":"10.3390\/medicina61040629","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T05:21:04Z","timestamp":1743398464000},"page":"629","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review"],"prefix":"10.3390","volume":"61","author":[{"given":"Sandra","family":"L\u00f3pez Gordo","sequence":"first","affiliation":[{"name":"General and Digestive Surgery Department, Maresme Health Consortium, 08304 Mataro, Spain"},{"name":"Unit of Human Anatomy and Embriology, Department of Morphological Sciences, Faculty of Medicine, Universitat Aut\u00f2noma de Barcelona, Cerdanyola del Vall\u00e8s, 08193 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Ramirez-Maldonado","sequence":"additional","affiliation":[{"name":"General and Digestive Surgery Department, Universitary Hospital of Tarragona Joan XXIII, 43005 Tarragona, Spain"},{"name":"Biomedicine Department, Rovira i Virgili University, 43007 Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8224-0037","authenticated-orcid":false,"given":"Maria Teresa","family":"Fernandez-Planas","sequence":"additional","affiliation":[{"name":"Radiology Department, Maresme Health Consortium, 08304 Mataro, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0435-671X","authenticated-orcid":false,"given":"Ernest","family":"Bombuy","sequence":"additional","affiliation":[{"name":"General and Digestive Surgery Department, Maresme Health Consortium, 08304 Mataro, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Memba","sequence":"additional","affiliation":[{"name":"General and Digestive Surgery Department, Universitary Hospital of Tarragona Joan XXIII, 43005 Tarragona, Spain"},{"name":"Biomedicine Department, Rovira i Virgili University, 43007 Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3307-4340","authenticated-orcid":false,"given":"Rosa","family":"Jorba","sequence":"additional","affiliation":[{"name":"General and Digestive Surgery Department, Universitary Hospital of Tarragona Joan XXIII, 43005 Tarragona, Spain"},{"name":"Biomedicine Department, Rovira i Virgili University, 43007 Tarragona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Working Group IAP\/APA Acute Pancreatitis Guidelines (2013). 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