{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T14:09:27Z","timestamp":1789740567224,"version":"4.0.1"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Endohepatology (the implementation of contemporary advanced endoscopy in hepatology) has reached a point where endoscopic ultrasound (EUS)\u2013guided liver biopsy, portal pressure gradient measurement, parenchymal elastography, and variceal screening and therapy can be integrated into a single procedural session of liver\u2010directed endoscopy. Concurrently, artificial intelligence (AI) has revolutionized luminal endoscopy and is advancing rapidly across hepatology imaging, digital pathology, and outcome prediction, yet its translation into the liver\u2010targeted endoscopic workflow has never been synthesized into a coherent domain. This narrative review maps this evolving convergence of AI and endohepatology across four functional pillars: intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support. As direct EUS\u2010specific AI evidence in the liver remains limited, the review serves as a forward\u2010looking roadmap for a work in progress that combines adjacent proof\u2010of\u2010concept from AI\u2010assisted EUS in nonhepatic indications, transabdominal AI elastography, and AI histopathology to present possible near\u2010term integration. To differentiate demonstrated capabilities from extrapolated and conceptual applications, a three\u2010tier readiness framework is proposed, summarized in a domain table and a clinical\u2010pathway figure. We find that, although the field is still very early\u2010stage, AI has strong potential to transform endohepatology into a single, machine\u2010driven platform for diagnostic and therapeutic success, provided that standardized datasets, prospective validation, and clear regulatory and governance standards are in place. The most immediate value will be in applications with transferable evidence, with hemodynamic and risk\u2010prediction applications following as device\u2010level data accrue.<\/jats:p>","DOI":"10.1111\/jgh.70757","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:55:09Z","timestamp":1789739709000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Endohepatology: Toward an Intelligent One\u2010Stop Shop for Liver\u2010Directed Endoscopy"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0026-0841","authenticated-orcid":false,"given":"Ahmed Abdallah","family":"Salman","sequence":"first","affiliation":[{"name":"Internal Medicine Department, Faculty of Medicine Cairo University  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