{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T20:35:52Z","timestamp":1793651752971,"version":"4.5.1"},"reference-count":94,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Liver diseases, including hepatitis, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), remain a major global health concern, with early and accurate diagnosis being essential for effective management. Imaging modalities such as ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) play a crucial role in non-invasive diagnosis, but their sensitivity and diagnostic accuracy can be limited. Recent advancements in artificial intelligence (AI) have improved imaging-based liver disease assessment by enhancing pattern recognition, automating fibrosis and steatosis quantification, and aiding in HCC detection. AI-driven imaging techniques have shown promise in fibrosis staging through US, CT, MRI, and elastography, reducing the reliance on invasive liver biopsy. For liver steatosis, AI-assisted imaging methods have improved sensitivity and grading consistency, while in HCC detection and characterization, AI models have enhanced lesion identification, classification, and risk stratification across imaging modalities. The growing integration of AI into liver imaging is reshaping diagnostic workflows and has the potential to improve accuracy, efficiency, and clinical decision-making. This review provides an overview of AI applications in liver imaging, focusing on their clinical utility and implications for the future of liver disease diagnosis.<\/jats:p>","DOI":"10.3389\/fmed.2025.1591523","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T05:25:28Z","timestamp":1745558728000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Artificial intelligence in imaging for liver disease diagnosis"],"prefix":"10.3389","volume":"12","author":[{"given":"Chenglong","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingling","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqin","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1038\/s41575-024-00967-4","article-title":"Liver diseases and hepatocellular carcinoma in the Asia-Pacific region: burden, trends, challenges and future directions","volume":"21","author":"Mak","year":"2024","journal-title":"Nat Rev Gastroenterol Hepatol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1186\/s12889-024-17948-6","article-title":"Global burden of liver cirrhosis and other chronic liver diseases caused by specific etiologies from 1990 to 2019","volume":"24","author":"Wu","year":"2024","journal-title":"BMC Public Health."},{"key":"B3","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/B978-0-443-30080-6.00021-3","article-title":"Liver diseases: genetic factors, current challenges, and future directions. 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