[1] Younossi Z M, Golabi P, Paik J M, et al. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review[J]. Hepatology, 2023, 77(4):1335-1347.
[2] Golabi P, Paik J M, AlQahtani S, et al. Burden of non-alcoholic fatty liver disease in Asia, the Middle East and North Africa: data from global burden of disease 2009-2019[J]. J Hepatol, 2021, 75(4):795-809.
[3] Taylor R S, Taylor R J, Bayliss S, et al. Association between fibrosis stage and outcomes of patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis[J]. Gastroenterology, 2020, 158(6):1611-1625.e12.
[4] Lu M Y, Chen B, Williamson D F K, et al. A visual-language foundation model for computational pathology[J]. Nat Med, 2024, 30(3):863-874.
[5] Lu M Y, Chen B, Williamson D F K, et al. A multimodal generative AI copilot for human pathology[J]. Nature, 2024, 634(8033):466-473.
[6] Vorontsov E, Bozkurt A, Casson A, et al. A foundation model for clinical-grade computational pathology and rare cancers detection[J]. Nat Med, 2024, 30(10):2924-2935.
[7] Nakatsuka T, Tateishi R, Sato M, et al. Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease[J]. Hepatology, 2025, 81(3):976-989.
[8] Fu Y, Jung A W, Torne R V, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis[J]. Nat Cancer, 2020, 1(8):800-810.
[9] Kiani A, Uyumazturk B, Rajpurkar P, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer[J]. NPJ Digital Medicine, 2020, 3:23.
[10] Acs B, Rantalainen M, and Hartman J. Artificial intelligence as the next step towards precision pathology[J]. J Intern Med, 2020, 288(1):62-81.
[11] Ratziu V, Yilmaz Y, Lazas D, et al. Aramchol improves hepatic fibrosis in metabolic dysfunction–associated steatohepatitis: Results of multimodality assessment using both conventional and digital pathology[J]. Hepatology, 2025, 81(3):932-946.
[12] Ratziu V, Francque S, Behling C A, et al. Artificial intelligence scoring of liver biopsies in a phase II trial of semaglutide in nonalcoholic steatohepatitis[J]. Hepatology, 2024, 80(1):173-185.
[13] Davison B A, Harrison S A, Cotter G, et al. Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials[J]. J Hepatol, 2020, 73(6):1322-1332.
[14] Forlano R, Mullish B H, Giannakeas N, et al. High-throughput, machinelearning–based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease[J]. Clin Gastroenterol Hepatol, 2020, 18(9):2081-2090.e9.
[15] Iyer J S, Juyal D, Le Q, et al. AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases[J]. Nat Med, 2024, 30(10):2914-2923.
[16] Pulaski H, Harrison S A, Mehta S S, et al. Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis[J]. Nat Med, 2025, 31(1):315-322.
[17] Ratziu V, Hompesch M, Petitjean M, et al. Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions[J]. J Hepatol, 2024, 80(2):335-351.
[18] Wang Y, Wong G L H, He F P, et al. Quantifying and monitoring fibrosis in non-alcoholic fatty liver disease using dual-photon microscopy[J]. Gut, 2020, 69(6):1116-1126.
[19] Sanyal A J, Ratziu V, Loomba R, et al. Results from a new efficacy and safety analysis of the REGENERATE trial of obeticholic acid for treatment of pre-cirrhotic fibrosis due to non-alcoholic steatohepatitis[J]. J Hepatol, 2023, 79(5):1110-1120.
[20] Lin H, Lee H W, Yip T C F, et al. Vibration-controlled transient elastography scores to predict liver-related events in steatotic liver disease[J]. JAMA, 2024, 331(15):1287-1297. |
