Boppana SH, Chandrashekar A, Sunkesula V. From hype to clinical translation: A tiered, readiness-based framework for artificial intelligence in gastrointestinal endoscopy. Artif Intell Gastrointest Endosc 2026; 7(2): 121109 [DOI: 10.37126/aige.121109]
Corresponding Author of This Article
Venkata Sunkesula, MD, Academic Fellow, Assistant Professor, Department of Gastroenterology and Hepatology, Case Western Reserve University, 2500 MetroHealth Drive, Cleveland, OH 44109, United States. kumarsvc@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Artificial Intelligence in Gastrointestinal Endoscopy
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2689-7164
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Boppana SH, Chandrashekar A, Sunkesula V. From hype to clinical translation: A tiered, readiness-based framework for artificial intelligence in gastrointestinal endoscopy. Artif Intell Gastrointest Endosc 2026; 7(2): 121109 [DOI: 10.37126/aige.121109]
From hype to clinical translation: A tiered, readiness-based framework for artificial intelligence in gastrointestinal endoscopy
Sri Harsha Boppana, Aditya Chandrashekar, Venkata Sunkesula
Sri Harsha Boppana, Department of Internal Medicine, Nassau University Medical Center, East Meadow, NY 11554, United States
Aditya Chandrashekar, Department of General Medicine, Bangalore Medical College and Research Institute, Bangalore 560002, Karnātaka, India
Venkata Sunkesula, Department of Gastroenterology and Hepatology, Case Western Reserve University, Cleveland, OH 44109, United States
Author contributions: Boppana SH, Chandrashekar A, and Sunkesula V designed the study and wrote and revised the manuscript; Boppana SH and Chandrashekar A performed the literature search and synthesis; and all authors have read and approved the final manuscript.
AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Venkata Sunkesula, MD, Academic Fellow, Assistant Professor, Department of Gastroenterology and Hepatology, Case Western Reserve University, 2500 MetroHealth Drive, Cleveland, OH 44109, United States. kumarsvc@gmail.com
Received: March 16, 2026 Revised: May 8, 2026 Accepted: June 8, 2026 Published online: September 8, 2026 Processing time: 172 Days and 9.1 Hours
Abstract
Artificial intelligence (AI) in gastrointestinal endoscopy is maturing unevenly. We organized this review around a predefined six-domain readiness framework: Evidence maturity, regulatory or health-system pathways, real-world deployment, workflow actionability, governance and monitoring, and generalizability. By these criteria, colonoscopy computer-aided detection is the sole clear tier 1 application, supported by multiple randomized trials and Food and Drug Administration clearances, though net patient-level value remains uncertain, and three concurrent guideline panels have issued discordant recommendations on identical evidence. Computer-aided diagnosis for optical polyp characterization remains tier 2 because two rigorous meta-analyses show no net benefit for the resect-and-discard strategy in routine practice. Upper gastrointestinal second-observer systems, AI-assisted procedural quality systems, capsule endoscopy reader-assist tools, and endoscopy-based Helicobacter pylori prediction are also tier 2, each limited by pathway uncertainty or limited deployment experience. Cholangioscopy AI, therapeutic endoscopy assistance, and endoscopic ultrasound-based pancreatic lesion analysis are tier 3, where technical performance consistently outpaces translational evidence. We also propose a prospective implementation checklist for regulators and endoscopy units evaluating emerging systems, and a prioritized five-year research agenda covering pathway-defined trials, representative datasets, human-factors safeguards, and post-deployment monitoring aligned with contemporary AI reporting standards.
Core Tip: Endoscopic artificial intelligence (AI) applications are at very different stages of translation. Colonoscopy computer-aided detection is the only system that meets readiness across all six domains we examined, yet guideline panels remain split on net patient value. Upper gastrointestinal second-observer systems, AI-assisted procedural quality systems, capsule endoscopy reader-assist tools, and endoscopy-based Helicobacter pylori prediction systems are technically strong but lack defined clinical pathways. Cholangioscopy, therapeutic, and pancreatic ultrasound AI are exploratory. Future translation will depend less on classifier accuracy and more on pathway definition, workflow integration, governance, and monitoring. We offer a tiered framework and a prospective checklist to guide implementation decisions.
