Digitizing large histopathology archives requires processing millions of scanned whole-slide images that must be validated rapidly. Automated organ-of-origin classification can accelerate quality control and enable early detection of mislabeled specimens. We developed a deep learning model that classifies the organ of origin from hematoxylin and eosin-stained slides using a single low-resolution thumbnail per slide in under 1 s. For training, we used thumbnails from 16,624 slides from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) archives, which contain mostly primary tumor resections. The images were categorized into 14 classes based on the most common primary sites in TCGA: Bladder, Brain, Breast, Colorectal, Kidney, Liver, Lung, Pancreas, Prostate, Skin, Stomach, Thyroid gland, Uterus, and Other (encompassing the remaining tissue types). We evaluated our approach on two independent external cohorts: a 5-class cohort with 2857 slides (Colorectal, Kidney, Liver, Pancreas, and Prostate) and a comprehensive 14-class cohort (12,348 slides). Results: The model achieved 90% balanced accuracy for the 5-class cohort and 62% for the full 14-class cohort. Notably, when considering only the predictions with high confidence, 53% of the large cohort could be classified with 74% balanced accuracy. Manual review of high-confidence misclassifications suggested that some may reflect errors in the ground truth rather than model error. Mean model inference time was 0.2 s per slide on an NVIDIA L4 GPU. Our deep learning approach demonstrates high classification performance with very low inference time, indicating its potential for real-time and cost-effective quality control in digital pathology.