Artificial Intelligence in IPMN diagnosis: bridging promise and clinical reality

Abstract

Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cystic tumors with malignant potential, requiring accurate risk stratification for management. Current diagnostic reliance on imaging and clinical guidelines is limited by subjectivity and moderate sensitivity. Artificial intelligence (AI), particularly machine learning and deep learning models, has demonstrated significant promise in distinguishing benign from high-risk lesions using CT, MRI, and endoscopic ultrasound. Studies have reported diagnostic accuracies as high as 94-99.6%, outperforming traditional clinical assessments. Despite these advances, widespread implementation is hindered by lack of standardized imaging protocols, methodological transparency, and prospective multicenter validation. Integrating AI into clinical practice and international guidelines may enhance precision diagnostics and improve prognostic accuracy in IPMN management.

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