Claudio De Angelis

Artificial intelligence is increasingly being applied to endoscopic ultrasonography for gastrointestinal subepithelial lesions, but the maturity of the evidence differs substantially across clinical tasks. This narrative review critically appraises contemporary studies of lesion detection and localization, wall-layer identification, diagnostic classification, pathological risk classification of gastrointestinal stromal tumors, human–artificial intelligence interaction, and potential management support. Thirty-one original studies published from 2021 through 2026 were included in the contemporary evidence map. Binary differentiation of gastrointestinal stromal tumors from leiomyomas or other selected comparators has the largest evidence base and includes multicenter, external, prospective, and real-time evaluations. Evidence for multiclass diagnosis, layer-of-origin assessment, small lesions, and pathological risk classification is less mature and is strongly influenced by case spectrum, reference standard, equipment, image selection, and validation design. No study has demonstrated that artificial intelligence improves tissue-acquisition strategy, surveillance, treatment selection, or patient outcomes in subepithelial lesions. Progress toward clinically useful decision support will require patient-level data separation, device- and center-held-out validation, analysis of complete examinations rather than curated images, explicit uncertainty handling, prospective human–artificial intelligence studies, and trials with endoscopy-specific management and safety outcomes.

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.

Endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) are both crucial for the endoscopic management of biliopancreatic diseases: the combination of their diagnostic and therapeutic potential is useful in many clinical scenarios, such as indeterminate biliary stenosis, biliary stones, chronic pancreatitis and biliary and pancreatic malignancies. This natural and evident convergence between EUS and ERCP, which by 2006 we were calling the “Endoscopic ultrasonography retrograde colangiopancreatography (EURCP) concept”, has become a hot topic in the last years, together with the implementation of the therapeutic possibilities of EUS (from EUS-guided necrosectomy to gastro-entero anastomoses) and with the return of ERCP to its original diagnostic purpose thanks to ancillary techniques (extraductal ultrasound (EDUS), intraductal ultrasound (IDUS), cholangiopancreatoscopy with biopsies and probe-based confocal laser endomicroscopy (pCLE)). In this literary review, we retraced the recent history of EUS and ERCP, reported examples of the clinical applicability of the EURCP concept and explored the option of performing the two procedures in only one endoscopic session, with its positive implications for the patient, the endoscopist and the health care system. In the last few years, we also evaluated the possibility of combining EUS and ERCP into a single endoscopic instrument in a single step, but certain obstacles surrounding this approach remain.