EP494 - Colonoscopy Quality Metrics in the age of Artificial Intelligence: Where do we go from here?

Purpose/Background: Traditional metrics such as adenoma detection rate (ADR), Cecal intubation rate (CIR), withdrawal time, and bowel prep adequacy are important benchmark according to ACG/ASGE and ESGE guidelines. The implementation of AI, image-enhanced endoscopy (IEE) and simulation-based training systems require revision in the current traditional assessment methods. Standards for quality indicators have historically been selected when they were measurable, showed variation in physician performance, applied across endoscopic services, impacted outcomes and quality of life. Also were well defined, reliable, and amenable to improvement at all levels.

Methods/Interventions: We analyzed current ACG/ASGE and ESGE guidelines together. And also an extensive search of published English literature from Jan 2000 to July 2025 was done. Search terms included colonoscopy quality metrics, adenoma detection rate, AI-assisted colonoscopy, image-enhanced endoscopy, and simulation-based training. The review focused on high-quality RCTs, meta-analyses and cost-effectiveness studies that evaluated AI, image-enhanced endoscopy (IEE) and quality metrics.

Results/Outcomes: The quality metric of ADR remains fundamental but studies show AI-assisted colonoscopy boosts ADR by 8–12% while lowering adenoma miss rates by almost 50% according to randomized controlled trials and meta-analyses. The CADeT-CS trial conducted in multiple sites across the U.S. demonstrated that AI technology reduced adenoma miss rates to 20% from 31% and sessile serrated lesion detection improved to 7% from 42%. A meta analysis of 28 RCTs demonstrated that pooled ADR rose to 36.6% from 25.2% (RR 1.44) while adenoma detection rates decreased by half. The detection of serrated and flat lesions becomes more effective through the use of image-enhanced endoscopy techniques including Narrow-band imaging (NBI), Linked-color imaging (LCI) and Blue-laser imaging (BLI). The conventional colonoscopy method already achieves more than 90% cecal intubation success in standard procedures but robotic and device-assisted systems have not shown superior result in this area The restricted availability of these technologies along with their low adoption rates indicates they still need time to get better to achieve substantial effects on core quality metrics. AI technology now uses mucosal inspection quality and fold-visualization indices to replace withdrawal time as a measure of thorough examination.

Conclusion/Discussion: AI systems have proven effective for both automated bowel preparation assessment and the determination of appropriate surveillance periods. The implementation of AI-assisted colonoscopy technology would prevent 7200 colorectal cancers and 2000 deaths annually in the U.S. while reducing healthcare costs by $290 million each year according to cost-effectiveness models. Finally, emerging measures such as sessile serrated lesion detection rate (SSLDR), patient experience indices, and competency-based training endpoints (simulation, VR, AI-feedback) are increasingly recognized as integral to defining high-quality colonoscopy.

The future benchmarks need to include AI-adjusted ADR, adenoma miss rate, sessile serrated lesion detection, competency-based training metrics, AI-enhanced bowel prep scoring and patient experience indicators. The inclusion of cost-effectiveness and safety monitoring will help maintain metrics that stay relevant and patient-focused while matching current technological capabilities.Updated Quality Metrics for Colonoscopy in the Era of Artificial Intelligence
Comparison of current guideline-based colonoscopy quality metrics (ACG/ASGE and ESGE) with proposed evidence-based updates incorporating AI, image-enhanced endoscopy (IEE), and digital standardization tools.