Tumor differentiation represents an important driver of the biological behavior of various forms of cancer. Histologic features of tumor differentiation in hepatocellular carcinoma (HCC) include cyto-architecture, immunohistochemical profile, and reticulin framework. In this study, we evaluate the performance of an artificial intelligence (AI)-based model in quantifying features of HCC tumor differentiation and predicting cancer-related outcomes. We developed a supervised AI model using a cloud-based, deep-learning platform (Aiforia Technologies) to quantify histologic features of HCC differentiation, including various morphologic parameters (nuclear density, area, circularity, chromatin pattern, and pleomorphism), mitotic figures, immunohistochemical markers (hepar-1 and glypican-3), and reticulin expression. We applied this AI model to patients undergoing HCC curative resection and assessed whether AI-based features added value to standard clinical and pathologic data in predicting HCC-related outcomes. 99 HCC resection specimens were included. Three AI-based histologic variables were most relevant to HCC prognostic assessment: 1. percent of tumor occupied by neoplastic nuclei (nuclear area %), 2. quantitative reticulin expression in the tumor, and 3. Hepar-1 low (i.e. expressed in less than 50% of the tumor)/glypican-3 positive immunophenotype. Statistical models that included these AI-based variables outperformed models with combined clinical-pathologic features for overall survival (C-indexes of 0.81 vs 0.68), disease-free survival (C-indexes of 0.73 vs 0.68), metastasis (C-indexes of 0.78 vs 0.65), and local recurrence (C-indexes of 0.72 vs 0.68) for all cases, with similar results in the subgroup analysis of WHO grade 2 HCCs. Our AI model serves as proof-of-concept that HCC differentiation can be objectively quantified digitally by assessing a combination of biologically relevant histopathologic features. In addition, several AI-derived features were independently predictive of HCC-related outcomes in our study population, most notably nuclear area %, hepar-low/glypican 3-negative phenotype, and decreasing levels of reticulin expression, highlighting the relevance of quantitative analysis of tumor differentiation features in this context.