Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI’s applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns—like data privacy and algorithmic bias—must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

Background/Objectives: Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total of 105 exams from five centers from both the European and American continents were included. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified with an expert consensus-based decision according to the Dallas Consensus, with division into a training and testing dataset in a patient-split design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: Pathological planimetry patterns were identified by an AdaBoost Classifier with 84.9% accuracy and a mean AUC-ROC of 0.92. Random Forest identified disorders of the esophagogastric junction opening with 86.7% accuracy and an AUC-ROC of 0.973. The Gradient Boosting Classifier identified disorders of the contractile response with 86.0% accuracy and an AUC-ROC of 0.933. Conclusions: In this study, integrating exams with different probe sizes and demographic contexts, a machine learning model accurately classified FLIP panometry exams according to the Dallas Consensus. AI-driven FLIP panometry could revolutionize the approach to this exam during an endoscopic procedure, optimizing exam accuracy, standardization, and accessibility, and transforming patient management.

Background/Objectives: Robotic capsule endoscopy (RCE) is an emerging technology that combines magnetically controlled gastric navigation with conventional capsule enteroscopy (CE), enabling a minimally invasive, comprehensive evaluation of the upper- and mid-gastrointestinal tract. This study aimed to characterize the real-world implementation and diagnostic performance of RCE in a European tertiary referral center. Methods: A retrospective, single-center analysis was conducted on adult patients (≥18 years) who underwent RCE (Omom RC) between June 2023 and July 2025. Eligible patients had a clinical indication for small bowel CE and a concurrent requirement for diagnostic gastroscopy or reassessment of known gastric lesions. The RCE protocol comprised an initial robotic-guided gastric examination followed by passive transit through the small bowel. Results: A total of 85 patients were included (52% female), with a median age of 49 years (IQR 40–64). The most common indications were suspected or established inflammatory bowel disease (57%) and iron deficiency anemia (31%). Gastric preparation was rated at least fair in 98% of cases, with good preparation in 38%. Median gastric transit time was 74 min (IQR 35–106). Relevant gastric findings were identified in 39 cases (46%), namely polyps (18%) and angiectasias (8%, including one with active bleeding), in addition to signs of chronic gastritis. Thirteen patients underwent subsequent endoscopy, resulting in seven therapeutic procedures. Small bowel findings were present in 60 patients (71%), including P3 (active bleeding) in 3% and P2 lesions (angiectasias, ulcers, tumors, varices) in 39%. One moderate adverse event occurred: small bowel capsule retention in a patient with multifocal neuroendocrine tumor and ileostomy, requiring endoscopic intervention. Conclusions: Robotic capsule endoscopy is a feasible tool for dual-region gastrointestinal evaluation. It enables high-quality gastric visualization, facilitates early detection of clinically actionable lesions, and maintains the diagnostic yield expected from standard small bowel CE. These findings support the integration of RCE into diagnostic pathways for patients requiring simultaneous gastric and small bowel assessment.