Patients with pancreatic cancer have low survival rates, largely because patients are diagnosed at an advanced stage. Current strategies for early detection, including imaging, blood tests, and genetic sequencing, have limited performance. Recent advances in artificial intelligence (AI) have shown that AI models can identify subtle pre-diagnostic imaging changes that may not be visible to radiologists, raising the possibility of earlier and more consistent pancreatic cancer detection. Despite this progress, real-world implementation of AI for pancreatic cancer detection remains limited. Most models struggle with reproducibility and generalizability across different institutions. Few have undergone prospective validation, and practical issues such as workflow integration, financial constraints, and continuous model monitoring remain unresolved. This article reviews the current state of AI for pancreatic cancer detection and outlines barriers beyond model specifics that must be addressed to enable clinical translation.