{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T10:06:05Z","timestamp":1790762765681,"version":"4.1.0"},"reference-count":103,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"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 is increasingly used in gastroenterology for endoscopic detection and characterisation, disease-activity assessment, risk prediction, treatment-response estimation and workflow support. However, high retrospective accuracy does not necessarily translate into reliable or beneficial clinical decision support. This structured narrative review examines the clinical development of artificial intelligence across gastrointestinal endoscopy, inflammatory bowel disease, hepatology and pancreaticobiliary diseases through three translational dimensions: explainability, generalisability and clinical actionability. Communication, human oversight and lifecycle governance are considered cross-cutting determinants of implementation. The\u00a0evidence is most mature in gastrointestinal endoscopy, particularly for computer-aided polyp detection, although benefits vary across operators, indications and practice settings. Applications in inflammatory bowel disease, hepatology and pancreaticobiliary care remain predominantly retrospective, with limited prospective evidence that model outputs alter management or improve patient outcomes. Explainability is commonly represented by heatmaps or feature-attribution methods, but rarely evaluated in terms of clinician understanding, calibrated trust or patient communication. External validation is becoming more frequent, yet geographic and technological transportability, calibration and performance under dataset shift remain insufficiently assessed. Prospective implementation and health-economic studies are uncommon, and available economic evidence is largely model-based.\u00a0Clinical artificial intelligence should therefore be evaluated as a sociotechnical intervention comprising the model, interface, users, workflow, communication process and associated care pathway. Translation requires clinically defined use cases, representative multicentre validation, human-centred evaluation, prospective assessment of benefits and harms, and continuous post-deployment surveillance. Clinical AI in gastroenterology should complement clinical expertise through transparent, contestable and continuously monitored decision support that preserves clinician accountability and supports informed, patient-centred decisions.<\/jats:p>","DOI":"10.3390\/diagnostics16193182","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:25:42Z","timestamp":1790760342000},"page":"3182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Gastroenterology: Towards Explainable, Generalisable and Clinically Actionable Decision Support"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8563-8342","authenticated-orcid":false,"given":"\u0130smail","family":"Baydili","sequence":"first","affiliation":[{"name":"Department of Audiovisual Techniques and Media Production, Vocational School of Technical Sciences, Firat University, Elazig 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-0946","authenticated-orcid":false,"given":"Burak","family":"Tasci","sequence":"additional","affiliation":[{"name":"Vocational School of Technical Sciences, \r\n\t  Firat University, Elazig 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering, Technology Faculty, \r\n\t  Firat University, Elazig 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5126-6445","authenticated-orcid":false,"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering, Technology Faculty, \r\n\t  Firat University, Elazig 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1053\/j.gastro.2023.05.050","article-title":"Global Burden of Digestive Diseases: A Systematic Analysis of the Global Burden of Diseases Study, 1990 to 2019","volume":"165","author":"Wang","year":"2023","journal-title":"Gastroenterology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6794","DOI":"10.3748\/wjg.v27.i40.6794","article-title":"Artificial intelligence in gastroenterology: A state-of-the-art review","volume":"27","author":"Engels","year":"2021","journal-title":"World J. 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