Prediction of the outcome of large B-cell lymphomas/high-grade B-cell lymphomas (DLBCLs/HGBCLs) is based on clinical parameters and molecular testing, for example, for rearrangements of MYC (MYC-R) and MYC-R in combination with BCL2 and BCL6 translocations (double/triple hit). However, the group of DLBCL/HGBCL with poor outcome is not confined to MYC-R lymphomas, and fluorescence in situ hybridization (FISH) testing for MYC-R misses several high-risk lymphomas. We aimed to understand if artificial intelligence (AI) trained to identify MYC-R will delineate a poor prognostic subgroup. We generated a collection of digital hematoxylin and eosin (H&E)-stained slides of DLBCL/HGBCL (N = 2018) annotated for MYC, BCL2, and BCL6 translocations. A multiple-instance deep learning AI model for the identification of MYC-R alone or as double/triple hit was established using 1035 H&E-stained slides and evaluated on an external test cohort (N = 499). A pretrained tumor tissue classifier improved reliability and interpretability by focusing the model on tumor areas. Our model score reflects a morphological "MYCness" in DLBCL/HGBCL and demonstrates a strong association with overall survival (OS) and progression-free survival (PFS) in the external test cohort. This was confirmed with an additional clinical test cohort (N = 484) without FISH labels. The AI model scores correlate with various molecular features of DLBCL/HGBCL, including BCL2 and MYC gene expression, and the high-grade gene expression signature, but also features of the tumor microenvironment. Multivariate analysis, adjusted for International Prognostic Index (IPI) factors, demonstrated the prognostic significance of our model in identifying high-risk cases for both PFS and OS.