Navigating the New Frontiers in Gastrointestinal Procedures: Technological Innovations and Clinical Implications

Gastrointestinal endoscopy is advancing rapidly, reshaping clinical practices through evolving evidence, technology, and safety-conscious workflows.

As technology progresses, computer-aided detection (CADe) systems in colonoscopy have been shown to increase adenoma detection rate (ADR) in randomized studies, while computer-aided diagnosis (CADx) tools are being evaluated for polyp characterization; against this backdrop, concerns about over-reliance remain salient. However, reporting has highlighted a countervailing risk: that routine dependence on AI could erode clinicians' independent detection skills over time. Balancing measurable benefits with safeguards for human expertise should therefore guide how AI is embedded in practice.

Similarly, GI endoscopy during pregnancy demands a nuanced approach to safeguard both maternal and fetal health. Many society guidelines advise, when feasible, scheduling non-urgent procedures in the second trimester and using the lowest effective sedation to limit fetal exposure. These points are consistent with society guidance on endoscopy in pregnancy.

Extending the safety lens from pregnancy protocols and AI oversight to daily workflows, recent studies and society recommendations are raising the bar for procedural safety and efficiency. One study explores optimization techniques for patient positioning and sedation to improve patient experience and procedural performance. In this context, optimization techniques for patient positioning and sedation may improve patient comfort and procedural efficiency in selected settings, as explored in the linked study.

Yet, embracing AI in endoscopic practice is not without challenges. Key hurdles include external validation, real-world generalizability, and ongoing performance and drift monitoring after deployment within clinical workflows. Prioritize role-appropriate automation that augments clinicians and safeguards skills, recognizing that AI can outperform humans in narrow detection tasks while clinicians retain oversight.

Key Takeaways:

• Use AI where it demonstrably lifts detection metrics while building safeguards and training to preserve independent clinician skills over time.
• Apply pregnancy-specific precautions (timing and minimal effective sedation) within multidisciplinary, patient-centered consent processes, aligning safety principles that also inform everyday workflow design.
• Translate the safety mindset to routine practice by testing positioning and sedation strategies that may enhance comfort and efficiency, and by validating tools in real-world settings.
• Design AI integration as role-appropriate automation with clinician oversight, coupled with continuous performance monitoring to catch drift and maintain quality.