Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter study aimed to validate a convolutional neural network (CNN) model using a large dataset of D-SOC images to automatically detect and characterize malignant BS. D-SOC exams from three centers-Centro Hospitalar Universitário de São João, Porto, Portugal (n = 123), Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain (n = 18), and New York University Langone Hospital, New York, USA (n = 23)-were included. Frames were categorized based on histopathology. The CNN's performance in detecting tumor vessels, papillary projections, nodules, and masses was assessed. The dataset was split into 90% training and 10% validation sets. Performance metrics included AUC, sensitivity, specificity, PPV, and NPV. Analysis of 96,020 images from 164 D-SOC exams (50,427 malignant strictures and 45,593 benign findings) showed the CNN achieved 92.9% accuracy, 91.7% sensitivity, 94.4% specificity, 95.1% PPV, 93.1% NPV, and an AUROC of 0.95. Accuracy rates for morphological features were 90.8% (papillary projections), 93.6% (nodules), 93.2% (masses), and 78.1% (tumor vessels). AI-driven CNN models hold promise for enhancing diagnostic accuracy in suspected biliary malignancies. This multicenter study contributes diverse datasets to ongoing research, supporting further AI applications in this patient population.