{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T19:01:29Z","timestamp":1791918089855,"version":"4.3.4"},"reference-count":131,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","dldjournalonline.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digestive and Liver Disease"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.dld.2026.03.005","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:15:19Z","timestamp":1774268119000},"page":"747-758","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence in hepatology: A position paper by the Italian Association for the Study of the Liver"],"prefix":"10.1016","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9615-7031","authenticated-orcid":false,"given":"Clara","family":"Balsano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7241-6329","authenticated-orcid":false,"given":"Anna","family":"Alisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8791-191X","authenticated-orcid":false,"given":"Patrizia","family":"Burra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenza","family":"Calvaruso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Calogero","family":"Camm\u00e0","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Campagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7715-2526","authenticated-orcid":false,"given":"Piergiorgio","family":"Donatelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Germani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5086-0514","authenticated-orcid":false,"given":"Alessio","family":"Gerussi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Giuffr\u00e8","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0561-7902","authenticated-orcid":false,"given":"Ana","family":"Lleo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"Panebianco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1399-6498","authenticated-orcid":false,"given":"Marcello","family":"Persico","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6466-1412","authenticated-orcid":false,"given":"Nicola","family":"Pugliese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3379-1756","authenticated-orcid":false,"given":"Silvia","family":"Rossi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.dld.2026.03.005_bib0001","series-title":"Ethics and governance of artificial intelligence for health","year":"2024"},{"key":"10.1016\/j.dld.2026.03.005_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.106014","article-title":"Modeling unknowns: a vision for uncertainty-aware machine learning in healthcare","volume":"203","author":"Campagner","year":"2025","journal-title":"Int J Med Inf"},{"key":"10.1016\/j.dld.2026.03.005_bib0003","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S2468-1253(24)00089-X","article-title":"WHO 2024 hepatitis B guidelines: an opportunity to transform care","volume":"9","author":"Easterbrook","year":"2024","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0004","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/s10462-025-11352-1","article-title":"AI-induced deskilling in medicine: a mixed-method review and research agenda for healthcare and beyond","volume":"58","author":"Natali","year":"2025","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.dld.2026.03.005_bib0005","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000651","article-title":"Bias in medical AI: implications for clinical decision-making","volume":"3","author":"Cross","year":"2024","journal-title":"PLOS Digit Health"},{"key":"10.1016\/j.dld.2026.03.005_bib0006","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.1038\/s41591-025-03983-2","article-title":"Generative artificial intelligence in medicine","volume":"31","author":"Teo","year":"2025","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0007","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.dld.2021.06.011","article-title":"The application of artificial intelligence in hepatology: a systematic review","volume":"54","author":"Balsano","year":"2022","journal-title":"Dig Liver Dis"},{"key":"10.1016\/j.dld.2026.03.005_bib0008","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/j.dld.2023.08.048","article-title":"Artificial intelligence and liver: opportunities and barriers","volume":"55","author":"Balsano","year":"2023","journal-title":"Dig Liver Dis"},{"key":"10.1016\/j.dld.2026.03.005_bib0009","doi-asserted-by":"crossref","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","article-title":"The false hope of current approaches to explainable artificial intelligence in health care","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.dld.2026.03.005_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106288","article-title":"The importance of being external. Methodological insights for the external validation of machine learning models in medicine","volume":"208","author":"Cabitza","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.dld.2026.03.005_bib0011","doi-asserted-by":"crossref","first-page":"985","DOI":"10.3171\/2019.12.SPINE191503","article-title":"Letter to the editor. Importance of calibration assessment in machine learning\u2013based predictive analytics","volume":"32","author":"Staartjes","year":"2020","journal-title":"J Neurosurg Spine"},{"key":"10.1016\/j.dld.2026.03.005_bib0012","series-title":"Handbook of research on machine learning applications and trends","first-page":"128","article-title":"Calibration of machine learning models","author":"Bella","year":"2010"},{"key":"10.1016\/j.dld.2026.03.005_bib0013","first-page":"1","article-title":"Metrics of calibration for probabilistic predictions","volume":"23","author":"Arrieta-Ibarra","year":"2022","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.dld.2026.03.005_bib0014","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jclinepi.2015.04.005","article-title":"Prediction models need appropriate internal, internal\u2013external, and external validation","volume":"69","author":"Steyerberg","year":"2016","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0015","article-title":"Interpretable and explainable machine learning: a methods-centric overview with concrete examples","volume":"13","author":"Marcinkevi\u010ds","year":"2023","journal-title":"WIREs Data Min Knowl Discov"},{"key":"10.1016\/j.dld.2026.03.005_bib0016","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Cynthia","year":"2019","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.dld.2026.03.005_bib0017","series-title":"The ACM Conference on Fairness, Accountability, and Transparency","article-title":"How can I choose an explainer? An application-grounded evaluation of post-hoc explanations","author":"S\u00e9rgio","year":"2021"},{"key":"10.1016\/j.dld.2026.03.005_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108042","article-title":"Evidence-based XAI: an empirical approach to design more effective and explainable decision support systems","volume":"170","author":"Famiglini","year":"2024","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0019","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen","year":"2023","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.dld.2026.03.005_bib0020","series-title":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","first-page":"514","article-title":"On the apparent conflict between individual and group fairness","author":"Binns","year":"2020"},{"key":"10.1016\/j.dld.2026.03.005_bib0021","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1038\/s41598-022-07939-1","article-title":"A clarification of the nuances in the fairness metrics landscape","volume":"12","author":"Castelnovo","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0022","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41746-020-0254-2","article-title":"Machine intelligence in healthcare\u2014perspectives on trustworthiness, explainability, usability, and transparency","volume":"3","author":"Cutillo","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0023","series-title":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","first-page":"1","article-title":"AI shall have no dominion: on how to measure technology dominance in AI-supported human decision-making","author":"Cabitza","year":"2023"},{"key":"10.1016\/j.dld.2026.03.005_bib0024","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1145\/163298.163309","article-title":"The productivity paradox of information technology","volume":"36","author":"Brynjolfsson","year":"1993","journal-title":"Commun ACM"},{"key":"10.1016\/j.dld.2026.03.005_bib0025","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1001\/jama.2023.25054","article-title":"Will generative artificial intelligence deliver on its promise in health care?","volume":"331","author":"Wachter","year":"2024","journal-title":"JAMA"},{"key":"10.1016\/j.dld.2026.03.005_bib0026","doi-asserted-by":"crossref","unstructured":"Brynjolfsson E., Rock D., Syverson C. Artificial Intelligence and the modern productivity paradox: a clash of expectations and statistics. Cambridge, MA: 2017. https:\/\/doi.org\/10.3386\/w24001.","DOI":"10.3386\/w24001"},{"key":"10.1016\/j.dld.2026.03.005_bib0027","unstructured":"HITECH act of 2009, Pub. L. 111-5, 123 stat.227 (Feb. 17, 2009). Available Online: https:\/\/www.HhsGov\/Sites\/Default\/Files\/Ocr\/Privacy\/Hipaa\/Understanding\/Coveredentities\/HitechactPdf n.d."},{"key":"10.1016\/j.dld.2026.03.005_bib0028","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph191912525","article-title":"The impact of meaningful use and electronic health records on hospital patient safety","volume":"19","author":"Trout","year":"2022","journal-title":"Int J Env Res Public Health"},{"key":"10.1016\/j.dld.2026.03.005_bib0029","doi-asserted-by":"crossref","first-page":"e25","DOI":"10.1097\/PTS.0000000000001081","article-title":"Electronic health record use issues and diagnostic error: a scoping review and framework","volume":"19","author":"Dixit","year":"2023","journal-title":"J Patient Saf"},{"key":"10.1016\/j.dld.2026.03.005_bib0030","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1001\/jama.2023.19138","article-title":"Electronic health records failed to make clinicians\u2019 lives easier\u2014will AI technology succeed?","volume":"330","author":"Hswen","year":"2023","journal-title":"JAMA"},{"key":"10.1016\/j.dld.2026.03.005_bib0031","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1001\/jama.2019.20866","article-title":"Challenges to the reproducibility of machine learning models in health care","volume":"323","author":"Beam","year":"2020","journal-title":"JAMA"},{"key":"10.1016\/j.dld.2026.03.005_bib0032","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12920-024-02072-6","article-title":"Challenges of reproducible AI in biomedical data science","volume":"18","author":"Han","year":"2025","journal-title":"BMC Med Genom"},{"key":"10.1016\/j.dld.2026.03.005_bib0033","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s00701-024-05892-8","article-title":"A critical moment in machine learning in medicine: on reproducible and interpretable learning","volume":"166","author":"Ciobanu-Caraus","year":"2024","journal-title":"Acta Neurochir (Wien)"},{"key":"10.1016\/j.dld.2026.03.005_bib0034","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s43856-024-00492-0","article-title":"Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis","volume":"4","author":"Kolbinger","year":"2024","journal-title":"Commun Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0035","series-title":"Deep reinforcement learning that matters","author":"Henderson","year":"2019"},{"key":"10.1016\/j.dld.2026.03.005_bib0036","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.dld.2026.03.005_bib0037","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep learning\u2014a technology with the potential to transform health care","volume":"320","author":"Hinton","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.dld.2026.03.005_bib0038","doi-asserted-by":"crossref","first-page":"6291","DOI":"10.1038\/s41598-025-90453-x","article-title":"Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders","volume":"15","author":"Sriram","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0039","first-page":"132","article-title":"The effects of data quality on machine learning performance on tabular data 2025","author":"Mohammed","year":"2025","journal-title":"Inform Syst"},{"key":"10.1016\/j.dld.2026.03.005_bib0040","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1111\/liv.15974","article-title":"Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes","volume":"44","author":"Giuffr\u00e8","year":"2024","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0041","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.cgh.2023.09.035","article-title":"Evaluating ChatGPT in medical contexts: the imperative to guard against hallucinations and partial accuracies","volume":"22","author":"Giuffr\u00e8","year":"2024","journal-title":"Clin Gastroenterol Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0042","article-title":"Bias patterns in the application of LLMs for clinical decision support: a comprehensive study","year":"2024","journal-title":"ArXiv"},{"key":"10.1016\/j.dld.2026.03.005_bib0043","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41746-023-00939-z","article-title":"Large language models propagate race-based medicine","volume":"6","author":"Omiye","year":"2023","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0044","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1038\/s41746-025-01790-0","article-title":"Cognitive bias in clinical large language models","volume":"8","author":"Mahajan","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0045","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1038\/s41746-024-01091-y","article-title":"Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework","volume":"7","author":"Kresevic","year":"2024","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0046","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1038\/s41746-025-01589-z","article-title":"Expert of experts verification and alignment (EVAL) framework for large language models safety in gastroenterology","volume":"8","author":"Giuffr\u00e8","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0047","article-title":"Guideline-enhanced large language models outperform physician-test takers on EASL campus quizzes multiple choice questions","volume":"7","author":"Giuffr\u00e8","year":"2025","journal-title":"JHEP Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0048","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s44401-024-00011-2","article-title":"Current and future state of evaluation of large language models for medical summarization tasks","volume":"2","author":"Croxford","year":"2025","journal-title":"Npj Health Syst"},{"key":"10.1016\/j.dld.2026.03.005_bib0050","doi-asserted-by":"crossref","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","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.dld.2026.03.005_bib0051","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2020-047709","article-title":"Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol","volume":"11","author":"Sounderajah","year":"2021","journal-title":"BMJ Open"},{"key":"10.1016\/j.dld.2026.03.005_bib0052","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"Cruz Rivera","year":"2020","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0053","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0054","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.crad.2022.09.131","article-title":"DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology","volume":"78","author":"Vasey","year":"2023","journal-title":"Clin Radiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0055","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in medical imaging (CLAIM): 2024 update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.dld.2026.03.005_bib0056","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1186\/s12916-025-04274-w","article-title":"Reporting guideline for Chatbot health advice studies: the CHART statement","volume":"23","author":"Huo","year":"2025","journal-title":"BMC Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0057","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-024-03425-5","article-title":"The TRIPOD-LLM reporting guideline for studies using large language models","volume":"31","author":"Gallifant","year":"2025","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0058","doi-asserted-by":"crossref","DOI":"10.1136\/bmjebm-2025-113825","article-title":"Reporting guideline for the use of generative artificial intelligence tools in medical research: the GAMER statement","author":"Luo","year":"2025","journal-title":"BMJ Evid Based Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104510","article-title":"The need to separate the wheat from the chaff in medical informatics","volume":"153","author":"Cabitza","year":"2021","journal-title":"Int J Med Inf"},{"key":"10.1016\/j.dld.2026.03.005_bib0060","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0061","doi-asserted-by":"crossref","DOI":"10.7189\/jogh.09.020318","article-title":"Artificial intelligence and algorithmic bias: implications for health systems","volume":"9","author":"Panch","year":"2019","journal-title":"J Glob Health"},{"key":"10.1016\/j.dld.2026.03.005_bib0063","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1093\/jrsssa\/qnae092","article-title":"The role of the third sector in public health service provision: evidence from 25,338 heterogeneous procurement datasets","volume":"188","author":"Rahal","year":"2025","journal-title":"J R Stat Soc Stat Soc"},{"key":"10.1016\/j.dld.2026.03.005_bib0064","unstructured":"Ministero della Salute. Piano nazionale della prevenzione 2020-2025. Ccm-NetworkIt 2020;2020:174."},{"key":"10.1016\/j.dld.2026.03.005_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.jrt.2024.100076","article-title":"C-XAI: a conceptual framework for designing XAI tools that support trust calibration","volume":"17","author":"Naiseh","year":"2024","journal-title":"J Responsible Technol"},{"key":"10.1016\/j.dld.2026.03.005_bib0066","first-page":"1","article-title":"A systematic review on fostering appropriate trust in human-AI interaction","volume":"26","author":"Mehrotra","year":"2023","journal-title":"ACM J Responsible Comput"},{"key":"10.1016\/j.dld.2026.03.005_bib0067","series-title":"NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg Scott","year":"2017"},{"key":"10.1016\/j.dld.2026.03.005_bib0068","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41746-020-00367-3","article-title":"Second opinion needed: communicating uncertainty in medical machine learning","volume":"4","author":"Kompa","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0069","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.jhep.2023.04.036","article-title":"Changing epidemiology, global trends and implications for outcomes of NAFLD","volume":"79","author":"Wong","year":"2023","journal-title":"J Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0070","first-page":"1","article-title":"Identification of hub gene for the pathogenic mechanism and diagnosis of MASLD by enhanced bioinformatics analysis and machine learning","volume":"20","author":"Lu","year":"2025","journal-title":"PLoS One"},{"key":"10.1016\/j.dld.2026.03.005_bib0071","article-title":"Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning","volume":"23","author":"Zhang","year":"2025","journal-title":"J Transl Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0072","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1111\/liv.16025","article-title":"AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: an exploratory study","volume":"44","author":"Yuan","year":"2024","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0073","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1038\/s41591-024-03301-2","article-title":"Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis","volume":"31","author":"Pulaski","year":"2025","journal-title":"Nat Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0074","first-page":"1","article-title":"Development and validation of a machine learning-based model for prediction of liver fibrosis and MASH","volume":"00","author":"Liu","year":"2025","journal-title":"J Clin Gastroenterol"},{"key":"10.1016\/j.dld.2026.03.005_bib0075","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1097\/HEP.0000000000000908","article-title":"Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD","volume":"80","author":"Charu","year":"2024","journal-title":"Hepatology"},{"key":"10.1016\/j.dld.2026.03.005_bib0076","doi-asserted-by":"crossref","first-page":"359","DOI":"10.2214\/AJR.20.24415","article-title":"Liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric segmentation tool: evaluation in 1204 healthy adults using unenhanced CT as a reference standard","volume":"217","author":"Pickhardt","year":"2021","journal-title":"Am J Roentgenol"},{"key":"10.1016\/j.dld.2026.03.005_bib0077","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1007\/s00330-024-11164-x","article-title":"Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method","volume":"35","author":"Meneses","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0078","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1080\/17434440.2025.2514764","article-title":"Deep learning without borders: recent advances in ultrasound image classification for liver diseases diagnosis","volume":"22","author":"Yousefzamani","year":"2025","journal-title":"Expert Rev Med Devices"},{"key":"10.1016\/j.dld.2026.03.005_bib0079","doi-asserted-by":"crossref","first-page":"6557","DOI":"10.1007\/s00330-023-09576-2","article-title":"Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes","volume":"33","author":"Meneses","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0080","doi-asserted-by":"crossref","DOI":"10.1111\/liv.16112","article-title":"Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: a comparative study of non-invasive tests and artificial intelligence-generated responses","volume":"45","author":"Wu","year":"2025","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0081","doi-asserted-by":"crossref","first-page":"722","DOI":"10.3390\/cancers17050722","article-title":"Revolutionizing MASLD: how artificial intelligence is shaping the future of liver care","volume":"17","author":"Pugliese","year":"2025","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.dld.2026.03.005_bib0082","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-09288-1","article-title":"Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease","volume":"15","author":"Kosick","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0083","doi-asserted-by":"crossref","DOI":"10.1097\/HC9.0000000000000683","article-title":"Generative AI in hepatology: transforming multimodal patient-generated data into actionable insights","volume":"9","author":"Lai","year":"2025","journal-title":"Hepatol Commun"},{"key":"10.1016\/j.dld.2026.03.005_bib0084","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1136\/gutjnl-2021-326271","article-title":"Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity","volume":"71","author":"Uche-Anya","year":"2022","journal-title":"Gut"},{"key":"10.1016\/j.dld.2026.03.005_bib0085","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s00428-024-03841-5","article-title":"A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)","volume":"485","author":"Ercan","year":"2024","journal-title":"Virchows Arch"},{"key":"10.1016\/j.dld.2026.03.005_bib0086","doi-asserted-by":"crossref","DOI":"10.1097\/HC9.0000000000000594","article-title":"Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease","volume":"9","author":"Khendek","year":"2025","journal-title":"Hepatol Commun"},{"key":"10.1016\/j.dld.2026.03.005_bib0087","article-title":"Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis","volume":"7","author":"Gerussi","year":"2025","journal-title":"JHEP Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0088","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/s41746-025-01870-1","article-title":"Automating liver biopsy segmentation with a robust, open-source tool for pathology research: the HOTSPoT model","volume":"8","author":"Cazzaniga","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0089","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.220160","article-title":"DeePSC: a deep learning model for automated diagnosis of primary sclerosing cholangitis at two-dimensional MR cholangiopancreatography","volume":"5","author":"Ragab","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.dld.2026.03.005_bib0090","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.dld.2022.10.015","article-title":"A quantitative MRCP-derived score for medium-term outcome prediction in primary sclerosing cholangitis","volume":"55","author":"Cristoferi","year":"2023","journal-title":"Dig Liver Dis"},{"key":"10.1016\/j.dld.2026.03.005_bib0091","article-title":"A composite score using quantitative magnetic resonance cholangiopancreatography predicts clinical outcomes in primary sclerosing cholangitis","volume":"5","author":"Vuppalanchi","year":"2023","journal-title":"JHEP Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0092","article-title":"Quantitative magnetic resonance cholangiopancreatography metrics are associated with disease severity and outcomes in people with primary sclerosing cholangitis","volume":"4","author":"Cazzagon","year":"2022","journal-title":"JHEP Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0093","article-title":"Deep learning analysis of MRI accurately detects early-stage perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis","author":"Singh","year":"2025","journal-title":"Hepatology"},{"key":"10.1016\/j.dld.2026.03.005_bib0094","doi-asserted-by":"crossref","DOI":"10.1016\/j.aohep.2024.101537","article-title":"Evaluation of four chatbots in autoimmune liver disease: a comparative analysis","volume":"30","author":"Daza","year":"2025","journal-title":"Ann Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0095","doi-asserted-by":"crossref","first-page":"914","DOI":"10.14309\/ajg.0000000000003179","article-title":"Is ChatGPT-4 a reliable tool in autoimmune hepatitis?","volume":"120","author":"Colapietro","year":"2025","journal-title":"Am J Gastroenterol"},{"key":"10.1016\/j.dld.2026.03.005_bib0096","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/S2468-1253(21)00472-6","article-title":"Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study","volume":"7","author":"Blach","year":"2022","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0097","article-title":"Role of artificial intelligence in the management of chronic hepatitis B infection","volume":"23","author":"Su","year":"2024","journal-title":"Clin Liver Dis (Hob)"},{"key":"10.1016\/j.dld.2026.03.005_bib0098","doi-asserted-by":"crossref","DOI":"10.1016\/j.gastrohep.2025.502362","article-title":"The efficiency of artificial intelligence for management and clinical decision-making in the identification of patients with hidden HCV infection (Intelligen-C strategy)","volume":"48","author":"Castro Urda","year":"2025","journal-title":"Gastroenterol Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0099","doi-asserted-by":"crossref","DOI":"10.3390\/app122111127","article-title":"An interpretable machine learning approach for hepatitis B diagnosis","volume":"12","author":"Obaido","year":"2022","journal-title":"Appl Sci"},{"key":"10.1016\/j.dld.2026.03.005_bib0100","article-title":"Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis","volume":"4","author":"Wong","year":"2022","journal-title":"JHEP Rep"},{"key":"10.1016\/j.dld.2026.03.005_bib0101","doi-asserted-by":"crossref","DOI":"10.1111\/liv.70349","article-title":"From guidelines to real-time conversation: expert-validated retrieval-augmented and fine-tuned <scp>GPT<\/scp>-4 for hepatitis C management","volume":"45","author":"Giuffr\u00e8","year":"2025","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0102","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.jhep.2019.02.014","article-title":"EASL clinical practice guidelines: drug-induced liver injury","volume":"70","author":"Andrade","year":"2019","journal-title":"J Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0103","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1111\/liv.15262","article-title":"Incidence and risk factors of drug-induced liver injury","volume":"42","author":"Li","year":"2022","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0104","doi-asserted-by":"crossref","DOI":"10.1111\/liv.70038","article-title":"Artificial intelligence: an emerging tool for studying drug-induced liver injury","volume":"45","author":"Niu","year":"2025","journal-title":"Liver Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0105","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1021\/acs.jcim.9b00143","article-title":"Gene expression data based deep learning model for accurate prediction of drug-induced liver injury in advance","volume":"59","author":"Feng","year":"2019","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.dld.2026.03.005_bib0106","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.3748\/wjg.v30.i18.2454","article-title":"Salivary metabolites are promising noninvasive biomarkers of drug-induced liver injury","volume":"30","author":"Yu","year":"2024","journal-title":"World J Gastroenterol"},{"key":"10.1016\/j.dld.2026.03.005_bib0107","doi-asserted-by":"crossref","DOI":"10.1016\/j.phrs.2023.107030","article-title":"Prior drug allergies are associated with worse outcome in patients with idiosyncratic drug-induced liver injury: a machine learning approach for risk stratification","volume":"199","author":"Niu","year":"2024","journal-title":"Pharmacol Res"},{"key":"10.1016\/j.dld.2026.03.005_bib0108","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2021.729834","article-title":"BERT-based natural language processing of drug labeling documents: a case study for classifying drug-induced liver injury risk","volume":"4","author":"Wu","year":"2021","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.dld.2026.03.005_bib0109","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2024.1401810","article-title":"Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost","volume":"7","author":"Chen","year":"2024","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.dld.2026.03.005_bib0110","doi-asserted-by":"crossref","first-page":"2436","DOI":"10.1056\/NEJMra2207599","article-title":"Alcohol-associated hepatitis","volume":"387","author":"Bataller","year":"2022","journal-title":"N Engl J Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0111","doi-asserted-by":"crossref","DOI":"10.1097\/HC9.0000000000000133","article-title":"Prevalence of alcohol-associated liver disease: a systematic review and meta-analysis","volume":"7","author":"Amonker","year":"2023","journal-title":"Hepatol Commun"},{"key":"10.1016\/j.dld.2026.03.005_bib0112","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1097\/HEP.0000000000000929","article-title":"Insights into ALD and AUD diagnosis and prognosis: exploring AI and multimodal data streams","volume":"80","author":"Narayanan","year":"2024","journal-title":"Hepatology"},{"key":"10.1016\/j.dld.2026.03.005_bib0113","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1111\/ajt.17059","article-title":"Artificial intelligence to identify harmful alcohol use after early liver transplant for alcohol-associated hepatitis","volume":"22","author":"Lee","year":"2022","journal-title":"Am J Transplant"},{"key":"10.1016\/j.dld.2026.03.005_bib0114","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1053\/j.gastro.2023.09.002","article-title":"Proteomic panels for alcohol-associated liver disease: accurate, but different enough from existing clinical tests?","volume":"165","author":"Mezzacappa","year":"2023","journal-title":"Gastroenterology"},{"key":"10.1016\/j.dld.2026.03.005_bib0115","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1097\/HEP.0000000000000883","article-title":"An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: a global cohort study","volume":"80","author":"Dunn","year":"2024","journal-title":"Hepatology"},{"key":"10.1016\/j.dld.2026.03.005_bib0116","doi-asserted-by":"crossref","DOI":"10.1016\/j.crad.2025.107042","article-title":"Diagnostic accuracy of computed tomography (CT)-based radiomics and artificial intelligence (AI) models in hepatocellular carcinoma: a systematic review and meta-analysis","volume":"89","author":"Sweta","year":"2025","journal-title":"Clin Radiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0117","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3748\/wjg.v30.i4.381","article-title":"Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: a quantitative review with radiomics quality score","volume":"30","author":"Brancato","year":"2024","journal-title":"World J Gastroenterol"},{"key":"10.1016\/j.dld.2026.03.005_bib0118","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3748\/wjg.v29.i1.43","article-title":"Current status and future perspectives of radiomics in hepatocellular carcinoma","volume":"29","author":"Miranda","year":"2023","journal-title":"World J Gastroenterol"},{"key":"10.1016\/j.dld.2026.03.005_bib0119","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s40644-025-00844-6","article-title":"LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning","volume":"25","author":"Stollmayer","year":"2025","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.dld.2026.03.005_bib0120","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1007\/s12072-022-10323-w","article-title":"Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images","volume":"16","author":"Chen","year":"2022","journal-title":"Hepatol Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0121","article-title":"Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images","volume":"13","author":"Zhang","year":"2024","journal-title":"Cancer Med"},{"key":"10.1016\/j.dld.2026.03.005_bib0122","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1007\/s00432-021-03617-3","article-title":"Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters","volume":"147","author":"Song","year":"2021","journal-title":"J Cancer Res Clin Oncol"},{"key":"10.1016\/j.dld.2026.03.005_bib0123","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s40644-024-00790-9","article-title":"Cross-institutional evaluation of deep learning and radiomics models in predicting microvascular invasion in hepatocellular carcinoma: validity, robustness, and ultrasound modality efficacy comparison","volume":"24","author":"Zhang","year":"2024","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.dld.2026.03.005_bib0124","article-title":"Performance of artificial intelligence models in predicting responsiveness of hepatocellular carcinoma to transarterial chemoembolization (TACE): a systematic review and meta-analysis","author":"Kiani","year":"2025","journal-title":"J Am Coll Radiol"},{"key":"10.1016\/j.dld.2026.03.005_bib0125","doi-asserted-by":"crossref","first-page":"148","DOI":"10.3390\/diagnostics15020148","article-title":"Artificial intelligence and radiomics in cholangiocarcinoma: a comprehensive review","volume":"15","author":"Zerunian","year":"2025","journal-title":"Diagnostics"},{"key":"10.1016\/j.dld.2026.03.005_bib0126","doi-asserted-by":"crossref","DOI":"10.2196\/69906","article-title":"The application status of radiomics-based machine learning in intrahepatic cholangiocarcinoma: systematic review and meta-analysis","volume":"27","author":"Xu","year":"2025","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.dld.2026.03.005_bib0127","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13244-023-01365-1","article-title":"Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII radiomics auditing group initiative","volume":"14","author":"Cannella","year":"2023","journal-title":"Insights Imaging"},{"key":"10.1016\/j.dld.2026.03.005_bib0128","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s12072-021-10291-7","article-title":"Application of machine learning in liver transplantation: a review","volume":"16","author":"Tran","year":"2022","journal-title":"Hepatol Int"},{"key":"10.1016\/j.dld.2026.03.005_bib0129","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1111\/ajt.16936","article-title":"Liver transplantation for severe alcoholic hepatitis: a multicenter Italian study","volume":"22","author":"Germani","year":"2022","journal-title":"Am J Transplant"},{"key":"10.1016\/j.dld.2026.03.005_bib0130","doi-asserted-by":"crossref","DOI":"10.1097\/HC9.0000000000000239","article-title":"Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: a qualitative study","volume":"7","author":"Strauss","year":"2023","journal-title":"Hepatol Commun"},{"key":"10.1016\/j.dld.2026.03.005_bib0131","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1136\/gut.2005.084434","article-title":"Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease","volume":"56","author":"Cucchetti","year":"2007","journal-title":"Gut"},{"key":"10.1016\/j.dld.2026.03.005_bib0132","doi-asserted-by":"crossref","DOI":"10.1016\/j.cgh.2024.12.010","article-title":"Gender-equity model for liver allocation using artificial intelligence (GEMA-AI) for waiting list liver transplant prioritization","author":"G\u00f3mez-Orellana","year":"2025","journal-title":"Clin Gastroenterol Hepatol"},{"key":"10.1016\/j.dld.2026.03.005_bib0133","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1111\/liv.16018","article-title":"The Equitable Benefit approach to guide the assessment of medical and psychosocial factors in liver transplant candidacy","volume":"44","author":"Crax\u00ec","year":"2024","journal-title":"Liver Int"}],"container-title":["Digestive and Liver Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1590865826003038?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1590865826003038?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:36:17Z","timestamp":1778859377000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1590865826003038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":131,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S1590865826003038"],"URL":"https:\/\/doi.org\/10.1016\/j.dld.2026.03.005","relation":{},"ISSN":["1590-8658"],"issn-type":[{"value":"1590-8658","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in hepatology: A position paper by the Italian Association for the Study of the Liver","name":"articletitle","label":"Article Title"},{"value":"Digestive and Liver Disease","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dld.2026.03.005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V. on behalf of Editrice Gastroenterologica Italiana S.r.l.","name":"copyright","label":"Copyright"}]}}