Abstract
Advancements in artificial intelligence (AI) are transforming medical imaging diagnostics, offering new possibilities for automated pancreatic tumor detection in computed tomography scans. Pancreatic ductal adenocarcinoma continues to be one of the most lethal malignancies, with early detection being critical for improving survival rates. Deep learning models can learn hierarchical feature representations directly from imaging data, enhancing tumor detection accuracy. However, variations in model performance, impaired generalizability, and limited interpretability remain critical barriers to clinical adoption. This article provides a comprehensive overview of deep learning-based pancreatic tumor detection, discussing fundamental concepts, recent advancements, and challenges for clinical adoption. Implementation of deep learning tumor detection models into imaging workflows holds promise for improving early detection rates of pancreatic tumors. Addressing issues of standardization, external validation, and model transparency will be essential to enable the integration of AI into pancreatic cancer screening and diagnostics, ultimately improving early detection and patient outcomes.