Abstract: Artificial intelligence, powered by deep-learning convolutional neural networks and advanced computer-vision algorithms, is now embedded across every phase of endoscopic retrograde cholangiopancreatography (ERCP): it fuses imaging and laboratory data to refine indication selection and risk stratification; it interprets cholangioscopic or fluoroscopic video in real time to detect malignant strictures, localize the ampulla, and alert for difficult cannulation, markedly lowering technical difficulty and radiation exposure; and it accurately forecasts post-ERCP pancreatitis, cholangitis, and other complications to enable individualized therapy. Artificial intelligence has already surpassed conventional diagnostics in accuracy, outperformed empirical models in predictive power, and compressed the learning curve for trainees. Generative adversarial networks promise to synthesize high fidelity ERCP images on demand, creating a "digital film library" for zero-risk training and scalable augmentation of rare-case datasets.