{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:18:35Z","timestamp":1787818715297,"version":"build-2784847793"},"reference-count":78,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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>\n                    Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease around the world, affecting 33.6% of the adult population (95% CI: 28.1%\u201339.5%;\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u200999.9%), or roughly one in three. The extent of the liver damage is variable, from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH, formerly NASH), cirrhosis and hepatocellular carcinoma (HCC). Early diagnosis is essential to prevent serious liver damage. Traditional diagnostic techniques such as liver biopsy, imaging, and biomarker testing are all invasive, costly, reduced sensitive to early-stage disease, and they also have variability among observers. Modern diagnostic and prognostic approaches based on the principles of Artificial Intelligence (AI) and specifically on machine learning (ML) and deep learning (DL) have enabled multimodal approaches integrating clinical, imaging and molecular data. This scoping review conducted per PRISMA-ScR guidelines, synthesizes findings from 73 studies (search window 2020\u20132026) across three dimensions: clinical data driven models, imaging-based classifiers (ultrasound, CT and MRI), and multi-omics (genomics, transcriptomics and proteomics) techniques. Moreover, emergence of models such as U-Net and LiverNet 2.x, classification models like DeepLiverNet and BiLSTM models, as well as transformer frameworks and the identification of biomarkers models are also described. This study also investigates challenges such as data heterogeneity, data interpretability, fairness and real-world clinical application. Finally, important areas of research opportunities and future directions are highlighted to present a developing clinically applicable, explainable and ethical AI solutions to manage MASLD.\n                  <\/jats:p>","DOI":"10.3389\/fmed.2026.1875846","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:45:51Z","timestamp":1787816751000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies\u2014a scoping review"],"prefix":"10.3389","volume":"13","author":[{"given":"M.","family":"Sujana","sequence":"first","affiliation":[{"name":"School of Computer Science Engineering and Information Systems, Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. N.","family":"Vivekananda","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Information Systems, Vellore Institute of Technology","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1097\/HEP.0000000000000520","article-title":"A multisociety delphi consensus statement on new fatty liver disease nomenclature","volume":"78","author":"Rinella","year":"2023","journal-title":"Hepatology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1016\/j.cgh.2025.02.006","article-title":"High global prevalence of steatotic liver disease and associated subtypes: a meta-analysis","volume":"23","author":"Ho","year":"2025","journal-title":"Clin Gastroenterol Hepatol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/s41575-023-00843-7","article-title":"Digital pathology for nonalcoholic steatohepatitis assessment","volume":"21","author":"Sanyal","year":"2024","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e30066","DOI":"10.2196\/30066","article-title":"Deep learning techniques for fatty liver using multi-view ultrasound images scanned by different scanners: development and validation study","volume":"9","author":"Kim","year":"2021","journal-title":"JMIR Med Inform"},{"key":"B5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.36469\/jheor.2024.92223","article-title":"Prevalence of nonalcoholic steatohepatitis and associated fibrosis stages among US adults using imaging-based vs biomarker-based noninvasive tests","volume":"11","author":"Fishman","year":"2024","journal-title":". Health Econ Outcomes Res"},{"key":"B6","doi-asserted-by":"publisher","first-page":"5304","DOI":"10.3390\/s21165304","article-title":"Cascaded deep learning neural network for automated liver steatosis diagnosis using ultrasound images","volume":"21","author":"Rhyou","year":"2021","journal-title":"Sensors"},{"key":"B7","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1186\/s12880-025-01805-y","article-title":"FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD","volume":"25","author":"Fan","year":"2025","journal-title":"BMC Med Imaging"},{"key":"B8","doi-asserted-by":"publisher","first-page":"953","DOI":"10.11124\/JBIES-21-00242","article-title":"Best practice guidance and reporting items for the development of scoping review protocols","volume":"20","author":"Peters","year":"2022","journal-title":"JBI Evid Synth"},{"key":"B9","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1016\/j.dsx.2021.03.019","article-title":"Non-alcoholic fatty liver disease and clinical outcomes in patients with COVID-19: a comprehensive systematic review and meta-analysis","volume":"15","author":"Singh","year":"2021","journal-title":"Diabetes Metab Syndr Clin Res Rev"},{"key":"B10","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12916-024-03315-0","article-title":"Global epidemiology of type 2 diabetes in patients with NAFLD or MAFLD: a systematic review and meta-analysis","volume":"22","author":"Cao","year":"2024","journal-title":"BMC Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s12072-022-10342-7","article-title":"The promise of artificial intelligence for predictive biomarkers in hepatology","volume":"16","author":"Bhat","year":"2022","journal-title":"Hepatol Int"},{"key":"B12","doi-asserted-by":"publisher","first-page":"4110","DOI":"10.3390\/jcm11144110","article-title":"Personalized medicine and machine learning: a roadmap for the future","volume":"11","author":"Sebastiani","year":"2022","journal-title":"J Clin Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.3390\/diagnostics15182310","article-title":"Computed tomography radiomics and machine learning for prediction of histology-based hepatic steatosis scores","volume":"15","author":"Chu","year":"2025","journal-title":"Diagnostics"},{"key":"B14","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1002\/hep.32655","article-title":"Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis","volume":"77","author":"Chang","year":"2023","journal-title":"Hepatology"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2587","DOI":"10.1007\/s11606-021-07197-3","article-title":"Nonalcoholic fatty liver disease underdiagnosis in primary care: what are we missing?","volume":"37","author":"Nielsen","year":"2022","journal-title":"J Gen Intern Med"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e0035","DOI":"10.1097\/HC9.0000000000000035","article-title":"Clinical decision support automates care gap detection among primary care patients with nonalcoholic fatty liver disease","volume":"7","author":"Spann","year":"2023","journal-title":"Hepatol Commun"},{"key":"B17","doi-asserted-by":"publisher","first-page":"5227","DOI":"10.3390\/ijerph18105227","article-title":"Non-alcoholic fatty liver disease: metabolic, genetic, epigenetic and environmental risk factors","volume":"18","author":"Juanola","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"B18","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10238-022-00799-z","article-title":"Liver biopsy for assessment of chronic liver diseases: a synopsis","volume":"23","author":"Chowdhury","year":"2023","journal-title":"Clin Exp Med"},{"key":"B19","doi-asserted-by":"publisher","first-page":"20240010","DOI":"10.1002\/VIW.20240010","article-title":"Advancements of non-invasive imaging technologies for the diagnosis and staging of liver fibrosis: present\u00a0and future","volume":"5","author":"Huang","year":"2024","journal-title":"View"},{"key":"B20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s12876-024-03160-8","article-title":"Comparative study of ultrasound attenuation analysis and controlled attenuation parameter in the diagnosis and grading of liver steatosis in non-alcoholic fatty liver disease patients","volume":"24","author":"Wang","year":"2024","journal-title":"BMC Gastroenterol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s43066-025-00442-y","article-title":"Comparison of FIB-4, APRI and AST\/ALT ratio with FibroScan in patients with NAFLD and type 2 diabetes: a single-center study from Bangladesh","volume":"15","author":"Alam","year":"2025","journal-title":"Egypt Liver J"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1457598","DOI":"10.3389\/fendo.2024.1457598","article-title":"Elevated ALT\/AST ratio as a marker for NAFLD risk and severity: insights from a cross-sectional analysis in the United States","volume":"15","author":"Xuan","year":"2024","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.3390\/biomedicines10051184","article-title":"Premenopausal syndrome and NAFLD: a new approach based on gender medicine","volume":"10","author":"Carrieri","year":"2022","journal-title":"Biomedicines"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1007\/s12602-024-10262-y","article-title":"Gut microbes in polycystic ovary syndrome and associated comorbidities; type 2 diabetes, non-alcoholic fatty liver disease (NAFLD), cardiovascular disease (CVD), and the potential of microbial therapeutics","volume":"16","author":"Singh","year":"2024","journal-title":"Probiotics Antimicrob Proteins"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e231007","DOI":"10.1148\/radiol.231007","article-title":"Association between liver MRI proton density fat fraction and liver disease risk","volume":"309","author":"Xia","year":"2023","journal-title":"Radiology"},{"key":"B26","doi-asserted-by":"publisher","first-page":"784221","DOI":"10.3389\/fped.2021.784221","article-title":"Magnetic resonance imaging-proton density fat fraction vs. Transient elastography-controlled attenuation parameter in diagnosing non-alcoholic fatty liver disease in children and adolescents: a meta-analysis of diagnostic accuracy","volume":"9","author":"Jia","year":"2022","journal-title":"Front Pediatr"},{"key":"B27","doi-asserted-by":"publisher","first-page":"34408","DOI":"10.1038\/s41598-025-17386-3","article-title":"Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis","volume":"15","author":"Nivethitha","year":"2025","journal-title":"Sci Rep"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1007\/s00261-025-05160-y","article-title":"Advanced liver fibrosis detection using a two-stage deep learning approach on standard T2-weighted MRI","volume":"51","author":"Gupta","year":"2026","journal-title":"Abdom Radiol"},{"key":"B29","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/s41747-023-00387-0","article-title":"CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease","volume":"7","author":"Hu","year":"2023","journal-title":"Eur Radiol Exp"},{"key":"B30","doi-asserted-by":"publisher","first-page":"e23118","DOI":"10.1002\/ima.23118","article-title":"Classification of the stages of nonalcoholic steatohepatitis via federated general visual representation learning","volume":"34","author":"Nergiz","year":"2024","journal-title":"Int J Imaging Syst Technol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"29819","DOI":"10.1038\/s41598-025-07862-1","article-title":"Inter- and intra-observer agreement in ultrasound diagnosis of steatotic liver disease: implications for screening in resource-limited settings","volume":"15","author":"Spencer-Sandino","year":"2025","journal-title":"Sci Rep"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1007\/s00261-025-05309-9","article-title":"Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset","volume":"51","author":"Bose","year":"2026","journal-title":"Abdom Radiol"},{"key":"B33","first-page":"204","article-title":"DEST: deep enhanced swin transformer toward better scoring for NAFLD","author":"Yan","year":"2022"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-025-04811-3","article-title":"Self-Supervised learning with BYOL for non-alcoholic fatty liver disease diagnosis using ultrasound imaging","volume":"19","author":"Buktash","year":"2025","journal-title":"Signal Image Video Process"},{"key":"B35","author":"Delfan","year":""},{"key":"B36","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.1038\/s41598-023-30750-5","article-title":"Machine learning classifiers for screening nonalcoholic fatty liver disease in general adults","volume":"13","author":"Qin","year":"2023","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1186\/s12876-025-04115-3","article-title":"Development and validation of a nomogram-based risk prediction model for non-alcoholic fatty liver disease (NAFLD): a logistic regression analysis in a physical examination population","volume":"25","author":"Lan","year":"2025","journal-title":"BMC Gastroenterol"},{"key":"B38","doi-asserted-by":"publisher","first-page":"e0329963","DOI":"10.1371\/journal.pone.0329963","article-title":"Automated classification of clinical diagnoses in electronic health records using transformer","volume":"20","author":"Dai","year":"2025","journal-title":"PLoS One"},{"key":"B39","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1111\/liv.15078","article-title":"Association of genetic variations with NAFLD in lean individuals","volume":"42","author":"Lin","year":"2022","journal-title":"Liver Int"},{"key":"B40","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1007\/s12072-022-10469-7","article-title":"Differential DNA methylation of steatosis and non-alcoholic fatty liver disease in adolescence","volume":"17","author":"Melton","year":"2023","journal-title":"Hepatol Int"},{"key":"B41","doi-asserted-by":"publisher","first-page":"121195","DOI":"10.1016\/j.lfs.2022.121195","article-title":"Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method","volume":"314","author":"Park","year":"2023","journal-title":"Life Sci"},{"key":"B42","doi-asserted-by":"publisher","first-page":"e10527","DOI":"10.1002\/advs.202410527","article-title":"Development and validation of machine learning-based marker for early detection and prognosis stratification of nonalcoholic fatty liver disease","volume":"12","author":"Xiao","year":"2025","journal-title":"Adv Sci"},{"key":"B43","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12967-021-03223-4","article-title":"Correction to: metabolic analysis of early nonalcoholic fatty liver disease in humans using liquid chromatography\u2013mass spectrometry","volume":"20","author":"Hu","year":"2022","journal-title":"J Transl Med"},{"key":"B44","doi-asserted-by":"publisher","first-page":"16122","DOI":"10.1038\/s41598-024-60768-2","article-title":"Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease","volume":"14","author":"Park","year":"2024","journal-title":"Sci Rep"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13748-025-00416-6","article-title":"Advanced prediction and diagnosis of liver diseases using multi-view heterogeneous graph neural networks","author":"Dongre","year":"2025","journal-title":"Prog Artif Intell"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1038\/s41588-022-01199-5","article-title":"Multiomics study of nonalcoholic fatty liver disease","volume":"54","author":"Sveinbjornsson","year":"2022","journal-title":"Nat Genet"},{"key":"B47","doi-asserted-by":"publisher","first-page":"1050439","DOI":"10.3389\/frai.2022.1050439","article-title":"Artificial intelligence applied to omics data in liver diseases: enhancing clinical predictions","volume":"5","author":"Baciu","year":"2022","journal-title":"Front Artif Intell"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1199087","DOI":"10.3389\/fgene.2023.1199087","article-title":"A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment","volume":"14","author":"Wekesa","year":"2023","journal-title":"Front Genet"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41870-025-02721-8","article-title":"Precision management of NAFLD using advanced machine learning: enhancing predictive diagnostics and personalized treatment","author":"Zeyauddin","year":"2025","journal-title":"Int J Inf Technol"},{"key":"B50","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"Br Med J"},{"key":"B51","doi-asserted-by":"publisher","first-page":"5894","DOI":"10.1007\/s00330-023-09515-1","article-title":"Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population","volume":"33","author":"Yang","year":"2023","journal-title":"Eur Radiol"},{"key":"B52","doi-asserted-by":"publisher","first-page":"10237","DOI":"10.3390\/app142210237","article-title":"Application of artificial intelligence in support of NAFLD diagnosis","volume":"14","author":"P\u0142udowski","year":"2024","journal-title":"Appl Sci"},{"key":"B53","doi-asserted-by":"publisher","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"B54","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat Med"},{"key":"B55","doi-asserted-by":"publisher","first-page":"e537","DOI":"10.1016\/S2589-7500(20)30218-1","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"2","author":"Liu","year":"2020","journal-title":"Lancet Digit Health"},{"key":"B56","doi-asserted-by":"publisher","first-page":"e549","DOI":"10.1016\/S2589-7500(20)30219-3","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"2","author":"Cruz Rivera","year":"2020","journal-title":"Lancet Digit Health"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41870-025-02716-5","article-title":"Toward transparent diagnosis of fatty liver disease: explainable AI-driven recommender systems using SHAP and LIME","author":"Zeyauddin","year":"2025","journal-title":"Int J Inf Technol"},{"key":"B58","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1007\/s44174-025-00414-1","article-title":"Machine learning and explainable AI for liver disease prediction: an integrated interpretability framework","volume":"4","author":"Khan","year":"2026","journal-title":"Biomed Mater Devices"},{"key":"B59","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1186\/s12875-024-02517-y","article-title":"Deploying a metabolic dysfunction-associated steatohepatitis consensus care pathway: findings from an educational pilot in three health systems","volume":"25","author":"Kumar","year":"2024","journal-title":"BMC Prim Care"},{"key":"B60","doi-asserted-by":"publisher","first-page":"7374","DOI":"10.1109\/JIOT.2023.3329061","article-title":"Federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions","volume":"11","author":"Rauniyar","year":"2024","journal-title":"IEEE Internet Things J"},{"key":"B61","doi-asserted-by":"publisher","first-page":"90792","DOI":"10.1109\/ACCESS.2022.3201876","article-title":"Adoption of federated learning for healthcare informatics: emerging applications and future directions","volume":"10","author":"Patel","year":"2022","journal-title":"IEEE Access"},{"key":"B62","doi-asserted-by":"publisher","first-page":"771219","DOI":"10.3389\/fmed.2022.771219","article-title":"Screening new blood indicators for non-alcoholic fatty liver disease (NAFLD) diagnosis of Chinese based on machine learning","volume":"9","author":"Wang","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"B63","doi-asserted-by":"publisher","first-page":"100443","DOI":"10.1016\/j.jhepr.2022.100443","article-title":"Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction","volume":"4","author":"Nam","year":"2022","journal-title":"JHEP Rep"},{"key":"B64","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1038\/s41746-025-01667-2","article-title":"A scoping review and evidence gap analysis of clinical AI fairness","volume":"8","author":"Liu","year":"2025","journal-title":"npj Digit Med"},{"key":"B65","doi-asserted-by":"publisher","first-page":"102423","DOI":"10.1016\/j.artmed.2022.102423","article-title":"A manifesto on explainability for artificial intelligence in medicine","volume":"133","author":"Combi","year":"2022","journal-title":"Artif Intell Med"},{"key":"B66","doi-asserted-by":"publisher","first-page":"S67","DOI":"10.1016\/j.gaceta.2020.12.019","article-title":"Artificial intelligence in healthcare: opportunities and risk for future","volume":"35","author":"Sunarti","year":"2021","journal-title":"Gac Sanit"},{"key":"B67","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1186\/s12913-024-11602-y","article-title":"Use of artificial intelligence tools in supporting decision-making in hospital management","volume":"24","author":"Alves","year":"2024","journal-title":"BMC Health Serv Res"},{"key":"B68","doi-asserted-by":"publisher","first-page":"111354","DOI":"10.4254\/wjh.v17.i11.111354","article-title":"Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: machine learning for non-invasive diagnosis and risk stratification","volume":"17","author":"Hegazy","year":"2025","journal-title":"World J Hepatol"},{"key":"B69","doi-asserted-by":"publisher","first-page":"111900","DOI":"10.3748\/wjg.v31.i47.111900","article-title":"Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: current status and future prospects","volume":"31","author":"Lou","year":"2025","journal-title":"World J Gastroenterol"},{"key":"B70","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1007\/s12325-024-02781-5","article-title":"Artificial intelligence in liver diseases: recent advances","volume":"41","author":"Lu","year":"2024","journal-title":"Adv Ther"},{"key":"B71","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1186\/s13244-024-01872-9","article-title":"Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis","volume":"15","author":"Zheng","year":"2024","journal-title":"Insights Imaging"},{"key":"B72","doi-asserted-by":"publisher","first-page":"1694191","DOI":"10.3389\/fnut.2025.1694191","article-title":"Integrated multi-omics analysis reveals the mechanisms of naringin in ameliorating high-fat diet-induced metabolic dysfunction-associated steatotic liver disease","volume":"12","author":"Sun","year":"2025","journal-title":"Front Nutr"},{"key":"B73","doi-asserted-by":"publisher","first-page":"8466","DOI":"10.3390\/jcm14238466","article-title":"Meta-analysis of AI integration in abdominal imaging for liver fibrosis and MASLD: evaluating diagnostic accuracy and clinical impact","volume":"14","author":"Pugliesi","year":"2025","journal-title":"J Clin Med"},{"key":"B74","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s43066-022-00224-w","article-title":"Artificial intelligence in nonalcoholic fatty liver disease","volume":"12","author":"Mahzari","year":"2022","journal-title":"Egypt Liver J"},{"key":"B75","doi-asserted-by":"publisher","first-page":"4192","DOI":"10.1007\/s11695-025-08096-w","article-title":"Prediction of metabolic dysfunction\u2013associated steatotic liver disease via advanced machine learning among Chinese han population","volume":"35","author":"Wu","year":"2025","journal-title":"Obes Surg"},{"key":"B76","doi-asserted-by":"publisher","first-page":"1630408","DOI":"10.3389\/fmed.2025.1630408","article-title":"A decade of review in global regulation and research of artificial intelligence medical devices (2015\u20132025)","volume":"12","author":"Zhang","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"B77","doi-asserted-by":"publisher","first-page":"241873","DOI":"10.1098\/rsos.241873","article-title":"Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use","volume":"12","author":"Pham","year":"2025","journal-title":"R Soc Open Sci"},{"key":"B78","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1186\/s12889-024-19882-z","article-title":"Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk","volume":"24","author":"Deng","year":"2024","journal-title":"BMC Public Health"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1875846\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T07:45:52Z","timestamp":1787816752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1875846\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,27]]},"references-count":78,"alternative-id":["10.3389\/fmed.2026.1875846"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1875846","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,27]]},"article-number":"1875846"}}