Jones, Patricia D

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.

INTRODUCTION: One of the primary goals of the Liver Cirrhosis Network (LCN) is to develop a cohort study to better understand and predict the risk of hepatic decompensation and other clinical and patient-reported outcomes among patients with Child A cirrhosis. METHODS: The LCN consists of a Scientific Data Coordinating Center and 10 clinical centers whose investigators populate multiple committees. The LCN Definitions and Measurements Committee developed preliminary definitions of cirrhosis and its complications by literature review, expert opinion, and reviewing definition documents developed by other organizations. The Cohort Committee developed the study protocol with the input of the steering committee. RESULTS: The LCN developed a prospective cohort study to describe and predict the rates of incident clinical events pertaining to first decompensation and patient-reported outcomes. The LCN developed a pragmatic definition of compensated cirrhosis incorporating clinical, laboratory, imaging, and histological criteria. Definitions of incident and recompensated ascites, overt hepatic encephalopathy, variceal hemorrhage, bleeding because of portal gastropathy, and hepatocellular carcinoma were also codified. DISCUSSION: The LCN Cohort Study design will inform the natural history of cirrhosis in contemporary patients with compensated cirrhosis. The LCN Definitions and Measures Committee developed criteria for the definition of cirrhosis to standardize entry into this multicenter cohort study and standardized criteria for liver-related outcome measures. This effort has produced definitions intended to be both sensitive and specific as well as easily operationalized by study staff such that outcomes critical to the LCN cohort are identified and reported in an accurate and generalizable fashion. REGISTRATION: NCT05740358.