Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology

Abstract

The use of artificial intelligence (AI) in endoscopic ultrasonography (EUS) is receiving increasing attention, particularly in the field of pancreatic diseases, where early and accurate diagnosis remains a major clinical challenge. This narrative review focuses on current and emerging applications of AI in pancreatic EUS, covering both image-based diagnostic support and the analysis of samples obtained through EUS-guided tissue acquisition. The first part of the review discusses how AI is being applied to improve EUS image interpretation, ranging from lesion detection to characterization and differentiation between benign and malignant pancreatic findings. The review also discusses early evidence and future perspectives for real-time procedural support, where diagnostic performance remains highly operator-dependent. The second part explores a less frequently discussed but equally relevant area: the use of AI in the analysis of cytological specimens obtained through EUS-guided fine-needle aspiration or fine-needle biopsy. Although cytopathology may appear to lie outside the traditional clinical scope of EUS, it represents an essential step in the diagnostic workflow of pancreatic diseases. Recent developments in AI-assisted digital cytology and pathology have shown promising potential to support and standardize cytological interpretation, with possible benefits in terms of diagnostic consistency, reproducibility, and turnaround time. By bridging imaging and pathology, AI may enhance the entire pancreatic EUS workflow, contributing to more efficient, accurate, and personalized diagnostic pathways in pancreatic disease management.