Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects

Abstract

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and management of biliopancreatic lesions. This review highlights the current state of AI-enhanced EUS (AI-EUS) for diagnosing solid and cystic pancreatic lesions, as well as biliary diseases. AI-driven models, including machine learning (ML) and deep learning (DL), have shown significant improvements in diagnostic accuracy, particularly in distinguishing pancreatic ductal adenocarcinoma (PDAC) from benign conditions and in the characterization of pancreatic cystic neoplasms. Advanced algorithms, such as convolutional neural networks (CNNs), enable precise image analysis, real-time lesion classification, and integration with clinical and genomic data for personalized care.

In biliary diseases, AI-assisted systems enhance bile duct visualization and streamline diagnostic workflows, minimizing operator dependency. Emerging applications, such as AI-guided EUS fine-needle aspiration (FNA) and biopsy (FNB), improve diagnostic yields while reducing errors. Despite these advancements, challenges remain, including data standardization, model interpretability, and ethical concerns regarding data privacy. Future developments aim to integrate multimodal imaging, real-time procedural support, and predictive analytics to further refine the diagnostic and therapeutic potential of AI-EUS.

AI-driven innovation in EUS stands poised to revolutionize pancreatico-biliary diagnostics, facilitating earlier detection, enhancing precision, and paving the way for personalized medicine in gastrointestinal oncology and beyond.