From hype to clinical translation: A tiered, readiness-based framework for artificial intelligence in gastrointestinal endoscopy

Vollständiger Abstract

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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.