Abstract:Artificial intelligence (AI) is rapidly being integrated into the entire workflow of pancreatic surgery, driving the transition toward precision and intelligent surgery in this highly complex field. In the preoperative setting, deep learning-based image segmentation and three-dimensional reconstruction enable precise delineation of pancreatic tumors and surrounding vasculature, facilitating surgical planning and resectability assessment. Intraoperatively, technologies such as augmented reality navigation and real-time tissue recognition assist in anatomical localization and risk identification, thereby improving surgical accuracy and safety. Postoperatively, AI-driven models integrating multi-source clinical data allow early prediction of complications and support individualized management. However, several challenges remain, including data heterogeneity, limited model interpretability, and the lack of standardized pathways for clinical translation. This review systematically summarizes the current applications of AI in preoperative planning, intraoperative navigation and decision-making, and postoperative complication management in pancreatic surgery, and further discusses existing challenges and future directions. Future efforts should focus on the development of high-quality multicenter standardized databases, multimodal and interpretable AI models, and closed-loop intelligent surgical systems integrating perception, decision-making, and execution, to facilitate the standardized implementation and clinical translation of AI technologies.