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For: Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021;73:2546-63. [PMID: 33098140 DOI: 10.1002/hep.31603] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Craxì L. Letter to the Editor: Comment on "The Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases". Hepatology 2021;74:1710. [PMID: 33185298 DOI: 10.1002/hep.31629] [Reference Citation Analysis]
2 Kezer CA, Shah VH, Simonetto DA. Advances in Predictive Modeling Using Machine Learning in the Field of Hepatology. Clin Liver Dis (Hoboken) 2021;18:288-91. [PMID: 34976373 DOI: 10.1002/cld.1148] [Reference Citation Analysis]
3 Miura K. Artificial intelligence has come to hepatology. Hepatol Res 2021;51:1031-2. [PMID: 34596311 DOI: 10.1111/hepr.13704] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Quan B, Li M, Lu S, Li J, Liu W, Zhang F, Chen R, Ren Z, Yin X. Predicting Disease-Specific Survival for Patients With Primary Cholangiocarcinoma Undergoing Curative Resection by Using a Decision Tree Model. Front Oncol 2022;12:824541. [DOI: 10.3389/fonc.2022.824541] [Reference Citation Analysis]
5 Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2021. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Reference Citation Analysis]
6 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
7 Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3(1): 21-27 [DOI: 10.35712/aig.v3.i1.21] [Reference Citation Analysis]
8 Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol 2021; 13(10): 1417-1427 [PMID: 34786176 DOI: 10.4254/wjh.v13.i10.1417] [Reference Citation Analysis]
9 Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [DOI: 10.4254/wjh.v13.i12.2039] [Reference Citation Analysis]
10 Ahn JC, Shah VH. Deep learning-based detection of hepatobiliary disorders in ophthalmic imaging. Lancet Digit Health 2021;3:e68-9. [PMID: 33509385 DOI: 10.1016/S2589-7500(20)30319-8] [Reference Citation Analysis]
11 Gazda J, Drotar P, Drazilova S, Gazda J, Gazda M, Janicko M, Jarcuska P. Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis. J Pers Med 2021;11:1090. [PMID: 34834442 DOI: 10.3390/jpm11111090] [Reference Citation Analysis]
12 Szabo G, Thursz M, Shah VH. Therapeutic advances in alcohol-associated hepatitis. J Hepatol 2022;76:1279-90. [PMID: 35589250 DOI: 10.1016/j.jhep.2022.03.025] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]