Coelho–Prabhu, Nayantara

INTRODUCTION: The American College of Gastroenterology assembled a multidisciplinary task force to evaluate the current state and future direction of artificial intelligence (AI) in gastroenterology, hepatology, and endoscopy leading to the development of consensus-based recommendations for responsible AI integration in clinical practice. METHODS: A total of 32 subject-matter experts and 12 industry partners, representing diverse practice settings and expertise, conducted subgroup literature reviews across 5 key areas (endoscopy, practice management clinical applications, training and education, inflammatory bowel disease and liver disease, ethics and equity). Draft statements were developed and rated on a 5-point Likert scale using a modified Delphi process. A consensus was set at ≥70% combined agreement. Nonconsensus items were revised and revoted electronically. RESULTS: A total of 43 statements, 40 (93%) reached consensus in round 1 and the remaining 3 achieved consensus after round 2. Evidence supports computer-aided detection improving adenoma detection rate and miss rate in controlled studies, with mixed real-world impact and insufficient long-term outcomes (e.g., interval colon cancer rate). Recommendations emphasize thorough validation and reduction of bias by heterogeneous Data sets. Outside endoscopy, ambient AI scribes, natural language processing (NLP)-enabled coding, workflow optimization, and previous authorization support show potential. Training recommendations endorse a structured AI curriculum while preserving independent procedural competence to avoid deskilling. In inflammatory bowel disease and hepatology, AI could help improve diagnostic accuracy, help predict risk of disease progression, and help guide therapy. Equity, governance, and reimbursement statements call for chain-of-custody data protections, specialty-society oversight, and payment models that reward quality and cost reduction. DISCUSSION: This consensus outlines how AI can augment rather than replace clinical expertise while promoting safety, transparency, interoperability, and equity. Priorities include pragmatic and prospective trials, multi-institutional data-sharing consortia, bias mitigation, and workforce training to enable trustworthy and clinically impactful AI adoption in gastroenterology, liver, and endoscopy care.

ABSTRACT Objectives Patients requiring long‐term enteral nutrition or continuous infusion of carbidopa/levodopa can benefit from jejunostomy tube placement. Recently, directly placed percutaneous transgastric jejunal tubes (TGJs) have been used instead of gastrostomy tubes with jejunal extensions (PEG‐Js) for enteral access. We aim to compare patient characteristics and outcomes after placement of TGJs placed via the introducer technique compared to PEG‐Js. Methods We performed a retrospective study of 141 patients (TGJ = 58, PEG‐J = 83) assessed at Mayo Clinic between 2010 and 2024. Patients were identified using a prospectively maintained procedure data registry. Demographic data, patient characteristics, procedural indications, complications, and first‐replacement date were gathered. Statistical analysis included the Wilcoxon rank sum test, chi‐square test, Fisher's exact test, and Kaplan‐Meier estimates. Patients receiving carbidopa/levodopa were excluded from complications analysis due to the carbidopa/levodopa tube's proprietary structure. A p ‐value of <0.05 was set as a threshold for significance. Results Our results demonstrated no difference in cumulative incidence of complications within 1 year for TGJs and PEG‐Js, p ‐value 0.48. Regarding time to first replacement, treating death as a competing risk factor, there was no statistically significant difference in cumulative incidence of replacement within 1 year for TGJs and PEG‐Js, p ‐value 0.389. Conclusions Our study demonstrates that both direct TGJs and PEG‐Js are safe options for long‐term jejunal feeding. More studies are needed to compare endoscopic to radiologically placed percutaneous transgastric jejunal feeding tubes.

ABSTRACT In response to the World Health Organization's (WHO) Global Initiative on Artificial Intelligence (AI) for Health, the World Endoscopy Organization (WEO) highlights the unique challenges and opportunities AI presents for gastrointestinal endoscopy, particularly in resource‐limited settings. While AI technologies have shown promise in improving diagnostic accuracy and efficiency in high‐resource environments, their implementation in low‐ and middle‐income countries is hindered by infrastructural, economic, regulatory, and training barriers. This commentary explores how these challenges may exacerbate existing healthcare disparities, emphasizing the need for localized datasets, affordable AI models, simplified regulatory frameworks, and workforce capacity building. The WEO supports WHO's call for equitable AI deployment and advocates for region‐specific solutions, including mobile and offline AI tools, public‐private partnerships, locally developed algorithms aligned with prevalent disease patterns, and a flexibly adapted regulatory framework. By leveraging WEO's training networks and fostering collaboration among governments, clinicians, and industry, the integration of AI into endoscopy can become more accessible and relevant to underserved populations. The commentary underscores that AI should not be seen as a luxury but as a tool to bridge global disparities in care quality. Ensuring responsible and inclusive AI integration requires both global coordination and context‐specific adaptations to truly benefit all healthcare systems.

Lay Summary Oral vancomycin shows promise in treating pouchitis in patients with primary sclerosing cholangitis and ulcerative colitis. In this study, vancomycin helped reduce stool frequency, urgency, and incontinence. More research is needed to confirm its long-term benefits and best treatment practices.

Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract resulting from an inappropriate immune response to an altered gut microbiome in genetically predisposed individuals. Endoscopy plays a central role in IBD management, aiding in diagnosis, disease staging, monitoring, and therapeutic guidance. Patients with IBD face an increased risk of colorectal neoplasia due to chronic inflammation. Artificial intelligence (AI)-based systems show promise in detecting and classifying dysplasia and neoplasia during endoscopic evaluation. While there have been several studies on the application of AI to detect and diagnose various types of neoplasia in the non-IBD population, the literature in patients with IBD is limited. We aim to summarize the current evidence on the application of AI technologies to detect IBD-associated neoplasia, highlighting potential benefits, limitations, and future directions. A comprehensive literature search was performed using the PubMed database to identify relevant studies from January 2010 to February 2025. Additional references were identified from the relevant articles’ bibliographies. AI-assisted endoscopy, particularly using machine learning and deep learning techniques, has shown promise in improving lesion detection rates and supporting real-time decision-making. Computer-aided detection systems may increase the sensitivity of dysplasia identification, while computer-aided diagnosis tools can aid in lesion characterization. Early studies suggest that AI can reduce interobserver variability, improve targeting of biopsies, and potentially lead to more personalized surveillance strategies. Although clinical data specific to IBD-related neoplasia remain limited compared to sporadic colorectal neoplasia, the integration of AI into endoscopic practice holds significant potential to enhance dysplasia detection and improve patient outcomes. Continued research, validation in IBD-specific cohorts, and integration with clinical workflows are essential to realize the full impact of AI in this setting.