Faculty Bibliography

BACKGROUND:The American College of Gastroenterology (ACG) 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 five key areas (endoscopy, practice management clinical applications, training and education, IBD 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. Non-consensus items were revised and re-voted 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 (CADe) 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 via heterogeneous datasets. Outside endoscopy, ambient AI scribes, NLP-enabled coding, workflow optimization, and prior authorization support show potential. Training recommendations endorse a structured AI curriculum while preserving independent procedural competence to avoid "deskilling". In IBD and hepatology, AI could help improve diagnostic accuracy, help predict risk for 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. CONCLUSIONS: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 GI, liver, and endoscopy care.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in the United States, with colonoscopy serving as the gold standard for both diagnosis and early intervention. While diminutive polyps (<5 mm) constitute most findings, only a small fraction exhibit advanced histological features. Optical diagnosis, which enables real-time classification of polyp histology through new technologies and the support of new strategies to leave low risk polyps in place (diagnose-and-leave) or resect without sending for formal pathology (resect-and-discard) have been studied as a cost-saving and effective strategy for diminutive polyps. There have been advances in imaging, such as narrow band imaging (NBI), but widespread adoption has yet to occur. The integration of artificial intelligence (AI), particularly computer-aided diagnosis (CADx) systems, has emerged as a promising tool to standardize optical diagnosis, reduce interobserver variability, and improve adherence to surveillance guidelines. However, barriers to widespread implementation persist, including concerns about medicolegal liability, financial disincentives, and skepticism of CADx accuracy. The goal of article is to review the current evidence surrounding optical diagnosis, review diagnostic accuracy, and evaluate the challenges of widespread clinical adoption.

BACKGROUND AND AIMS/OBJECTIVE:Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There is uncertainty regarding how different CADe algorithms may perform. No objective methodology exists for comparing different algorithms. We aimed to identify priority scoring metrics for CADe evaluation and comparison. METHODS:A modified Delphi approach was used. Twenty-five global leaders in CADe in colonoscopy, including endoscopists, researchers, and industry representatives, participated in an online survey over the course of 8 months. Participants generated 121 scoring criteria, 54 of which were deemed within the study scope and distributed for review and asynchronous e-mail-based open comment. Participants then scored criteria in order of priority on a 5-point Likert scale during ranking round 1. The top 11 highest priority criteria were re-distributed, with another opportunity for open comment, followed by a final round of priority scoring to identify the final 6 criteria. RESULTS:Mean priority scores for the 54 criteria ranged from 2.25 to 4.38 after the first ranking round. The top 11 criteria after round 1 of ranking yielded mean priority scores ranging from 3.04 to 4.16. The final 6 highest priority criteria, including a tie for first-place ranking, were (1, tied) sensitivity (average, 4.16) and (1, tied) separate and independent validation of the CADe algorithm (average, 4.16); (3) adenoma detection rate (average, 4.08); (4) false-positive rate (average, 4.00); (5) latency (average, 3.84); and (6) adenoma miss rate (average, 3.68). CONCLUSIONS:This is the first reported international consensus statement of priority scoring metrics for CADe in colonoscopy. These scoring criteria should inform CADe software development and refinement. Future research should validate these metrics on a benchmark video data set to develop a validated scoring instrument.

This document is an update to the 2014 recommendations for optimizing the adequacy of bowel cleansing for colonoscopy from the US Multi-Society Task Force on Colorectal Cancer, which represents the American College of Gastroenterology, the American Gastroenterological Association, and the American Society for Gastrointestinal Endoscopy. The US Multi-Society Task Force developed consensus statements and key clinical concepts addressing important aspects of bowel preparation for colonoscopy. The majority of consensus statements focus on individuals at average risk for inadequate bowel preparation. However, statements addressing individuals at risk for inadequate bowel preparation quality are also provided. The quality of a bowel preparation is defined as adequate when standard screening or surveillance intervals can be assigned based on the findings of the colonoscopy. We recommend the use of a split-dose bowel preparation regimen and suggest that a 2 L regimen may be sufficient. A same-day regimen is recommended as an acceptable alternative for individuals undergoing afternoon colonoscopy, but we suggest that a same-day regimen is an inferior alternative for individuals undergoing morning colonoscopy. We recommend limiting dietary restrictions to the day before a colonoscopy, relying on either clear liquids or low-fiber/low-residue diets for the early and midday meals. We suggest the adjunctive use of oral simethicone for bowel preparation before colonoscopy. Routine tracking of the rate of adequate bowel preparations at the level of individual endoscopists and at the level of the endoscopy unit is also recommended, with a target of >90% for both rates.

This document is an update to the 2014 recommendations for optimizing the adequacy of bowel cleansing for colonoscopy from the US Multi-Society Task Force on Colorectal Cancer, which represents the American College of Gastroenterology, the American Gastroenterological Association, and the American Society for Gastrointestinal Endoscopy. The US Multi-Society Task Force developed consensus statements and key clinical concepts addressing important aspects of bowel preparation for colonoscopy. The majority of consensus statements focus on individuals at average risk for inadequate bowel preparation. However, statements addressing individuals at risk for inadequate bowel preparation quality are also provided. The quality of a bowel preparation is defined as adequate when standard screening or surveillance intervals can be assigned based on the findings of the colonoscopy. We recommend the use of a split-dose bowel preparation regimen and suggest that a 2 L regimen may be sufficient. A same-day regimen is recommended as an acceptable alternative for individuals undergoing afternoon colonoscopy, but we suggest that a same-day regimen is an inferior alternative for individuals undergoing morning colonoscopy. We recommend limiting dietary restrictions to the day before a colonoscopy, relying on either clear liquids or low-fiber/low-residue diets for the early and midday meals. We suggest the adjunctive use of oral simethicone for bowel preparation before colonoscopy. Routine tracking of the rate of adequate bowel preparations at the level of individual endoscopists and at the level of the endoscopy unit is also recommended, with a target of >90% for both rates.

This document is an update to the 2014 recommendations for optimizing the adequacy of bowel cleansing for colonoscopy from the US Multi-Society Task Force on Colorectal Cancer, which represents the American College of Gastroenterology and the American Society for Gastrointestinal Endoscopy. The US Multi-Society Task Force developed consensus statements and key clinical concepts addressing important aspects of bowel preparation for colonoscopy. The majority of consensus statements focus on individuals at average risk for inadequate bowel preparation. However, statements addressing individuals at risk for inadequate bowel preparation quality are also provided. The quality of a bowel preparation is defined as adequate when standard screening or surveillance intervals can be assigned based on the findings of the colonoscopy. We recommend the use of a split-dose bowel preparation regimen and suggest that a 2 L regimen may be sufficient. A same-day regimen is recommended as an acceptable alternative for individuals undergoing afternoon colonoscopy, but we suggest that a same-day regimen is an inferior alternative for individuals undergoing morning colonoscopy. We recommend limiting dietary restrictions to the day before a colonoscopy, relying on either clear liquids or low-fiber/low-residue diets for the early and midday meals. We suggest the adjunctive use of oral simethicone for bowel preparation before colonoscopy. Routine tracking of the rate of adequate bowel preparations at the level of individual endoscopists and at the level of the endoscopy unit is also recommended, with a target of >90% for both rates.

BACKGROUND AND AIMS/OBJECTIVE:The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently. METHODS:A modified Delphi process was used to develop these consensus statements. RESULTS:Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology. CONCLUSIONS:The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.