Aim of review: This review synthesizes recent advances in AI for the early screening and diagnosis of pancreatic cancer. We focus on how AI enables population-level and high-risk prediction, augments diagnostic assessment in patients with suspicious clinical, imaging, or molecular findings, and supports precision stratification through multimodal integration of radiologic imaging, circulating biomarkers, and longitudinal electronic health records (EHRs). 

 Key scientific concepts of review: Advances span three domains. In imaging, deep learning models-including convolutional neural networks, transformer architectures, and self-configuring segmentation frameworks-improve pancreas segmentation, lesion detection, and classification, with several systems demonstrating radiologist-level performance in retrospective multicenter studies. In biomarker discovery, machine learning approaches such as LASSO, random forest, and XGBoost facilitate high-dimensional feature selection from transcriptomic, metabolomic, and exosomal data, enabling composite diagnostic signatures beyond CA19-9. In longitudinal EHR analysis, temporal deep learning models identify latent disease trajectories and predict pancreatic cancer risk months to years before clinical diagnosis. Despite these advances, most models remain retrospectively validated and face limitations related to data heterogeneity, interpretability, and cross-population generalizability. 

 Conclusion: AI strengthens early detection through multimodal integration, risk-adapted stratification, and data-driven clinical support aligned with precision medicine. Its near-term value lies in augmenting detection among high-risk populations rather than enabling universal screening or autonomous diagnosis. Prospective multicenter validation and improved model transparency are critical for translation into routine practice.