Artificial intelligence in endoscopy: a call for joint leadership between ESGE and Endoscopy

Key Takeaways for GI Nurses

• Artificial intelligence (AI)-assisted polyp detection tools are increasingly integrated into endoscopy suites, and nurses will play a key role in setting up, troubleshooting, and communicating these systems' outputs during procedures.
• AI is extending beyond detection into documentation and workflow management, meaning nursing responsibilities around charting, quality metrics, and scheduling may be directly affected by these new systems.
• Professional societies such as ESGE are calling for unified leadership on AI implementation, signaling that standardized guidelines and training expectations for endoscopy teams—including nurses—are likely forthcoming.
• Nurses should begin familiarizing themselves with AI terminology and basic functionality now, as early adoption and comfort with these tools will support smoother integration into daily practice and better patient communication.

Clinical Relevance

The integration of AI into endoscopic practice represents a significant shift in how GI units will operate, and nursing staff are positioned at the center of this transition. During procedures, AI-assisted polyp detection systems provide real-time visual cues to endoscopists, but nurses often serve as the first point of contact for monitoring equipment alerts, assisting with system calibration, and helping troubleshoot technical issues that arise mid-procedure. Understanding the basic logic behind these computer-aided detection (CADe) and characterization (CADx) tools allows nurses to anticipate workflow disruptions and maintain procedural efficiency without compromising patient safety or comfort.

Beyond the procedure room, AI's expansion into documentation and workflow management has direct implications for nursing duties related to charting, quality assurance, and scheduling. If AI systems begin auto-populating procedure notes, flagging incomplete documentation, or optimizing room turnover and case sequencing, nurses will need to adapt existing workflows to incorporate these tools while still ensuring accuracy and regulatory compliance. This shift may also change how nurses track quality metrics such as adenoma detection rates, as AI-generated data could supplement or validate manual reporting processes currently managed by nursing and endoscopy staff.

The call for joint leadership between ESGE and the journal Endoscopy underscores a broader recognition that successful AI adoption requires coordinated input from all stakeholders—not just physicians and technology developers, but also the nursing and allied health professionals who manage day-to-day unit operations. This suggests upcoming guidelines, training modules, or competency standards may be developed with multidisciplinary input, creating opportunities for nurses to contribute their frontline perspective and shape how AI tools are implemented safely and effectively across diverse practice settings.

Bottom Line

AI is becoming a permanent fixture in endoscopy—affecting polyp detection, documentation, and unit workflow—and GI nurses should proactively build familiarity with these emerging tools, as professional societies are actively working to establish standardized, collaborative approaches to their implementation that will shape future nursing roles and responsibilities in the endoscopy suite.