This American College of Gastroenterology (ACG)–led modified Delphi consensus provides a comprehensive framework for responsible integration of artificial intelligence (AI) into gastroenterology, hepatology, and endoscopy practice. A multidisciplinary task force of 32 experts and 12 industry partners reviewed the literature across five domains: endoscopy, clinical/practice management applications, IBD and liver disease, training/education, and ethics/equity.
This American College of Gastroenterology (ACG)–led modified Delphi consensus provides a comprehensive framework for responsible integration of artificial intelligence (AI) into gastroenterology, hepatology, and endoscopy practice. A multidisciplinary task force of 32 experts and 12 industry partners reviewed the literature across five domains: endoscopy, clinical/practice management applications, IBD and liver disease, training/education, and ethics/equity.
Out of 43 proposed statements, 100% ultimately reached consensus (≥70% agreement). In endoscopy, strong evidence supports computer-aided detection (CADe) for improving adenoma detection rates and reducing miss rates in controlled settings, though real-world impact and long-term outcomes (e.g., interval cancer reduction) remain uncertain. The panel emphasizes robust validation using heterogeneous datasets to mitigate bias.
Beyond endoscopy, promising applications include ambient AI scribes, natural language processing for coding, workflow optimization, and prior authorization support. In IBD and hepatology, AI shows potential in improving diagnostic accuracy, risk stratification, and therapeutic guidance.
Importantly, the consensus stresses that AI should augment—not replace—clinical expertise. Recommendations include structured AI curricula for trainees while preserving independent procedural competence to prevent “deskilling.” Ethical priorities focus on data governance, chain-of-custody protections, bias mitigation, specialty oversight, and equitable reimbursement models.
Future priorities include prospective pragmatic trials, multi-institutional data-sharing consortia, and transparent validation standards to ensure safe, effective, and equitable AI adoption in gastrointestinal practice.
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