Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials. Hanna Pulaski, Stephen A. Harrison, Shraddha S. Mehta, Arun J. Sanyal, Marlena C. Vitali, Laryssa C. Manigat, Hypatia Hou, Susan P. Madasu Christudoss, Sara M. Hoffman, Adam Stanford-Moore, Robert Egger, Jonathan Glickman, Murray Resnick, Neel Patel, Cristin E. Taylor, Robert P. Myers, Chuhan Chung, Scott D. Patterson, Anne-Sophie Sejling, Anne Minnich, Vipul Baxi, G. Mani Subramaniam, Quentin M. Anstee, Rohit Loomba, Vlad Ratziu, Michael C. Montalto, Nick P. Anderson, Andrew H. Beck & Katy E. Wack
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