Artificial intelligence in gastrointestinal endoscopy: From technical performance to clinical translation

Key Takeaways for GI Nurses

• Artificial intelligence (AI) tools, particularly computer-aided polyp detection (CADe) systems, are increasingly integrated into live colonoscopy procedures, meaning nurses assisting in these cases will interact with AI-generated visual cues in real time.
• AI is expanding beyond detection into documentation support and workflow management, which may change how procedure notes, lesion characterization, and quality metrics are captured during and after endoscopy.
• Successful clinical translation of AI depends not just on technical accuracy but on how well these systems integrate into existing endoscopy unit workflows—an area where nursing input and adaptability are essential.
• GI nurses should anticipate a growing role in troubleshooting, calibrating, and contextualizing AI alerts for physicians, requiring updated training and comfort with new technology platforms.

Clinical Relevance

The integration of AI into gastrointestinal endoscopy represents a significant shift in how procedures are performed and documented, with direct implications for the nursing role in the procedure room. As CADe systems become more common for polyp detection, nurses are often the first point of contact when technical issues arise—whether it's system calibration, interpreting on-screen prompts, or communicating AI findings to the physician during time-sensitive moments of the procedure. Understanding the basic function and limitations of these tools allows nurses to serve as effective liaisons between the technology and the clinical team, supporting both procedural efficiency and patient safety.

Beyond detection, AI's expanding role in documentation and workflow management has practical implications for day-to-day unit operations. Nurses frequently manage charting, quality metrics, and procedural logs, and as AI-assisted documentation tools mature, nursing staff may need to adapt their charting workflows to incorporate AI-generated data points—such as automated lesion measurements or detection rates—into existing electronic health record systems. This shift requires not only technical familiarity but also critical thinking about data accuracy and the nurse's role in verifying AI-assisted outputs before they become part of the permanent medical record.

From a professional development standpoint, this evolution signals that AI literacy is becoming a relevant competency for endoscopy nurses, not just physicians. Units that invest in staff training around AI tools—how they work, their known limitations, and appropriate human oversight—will likely see smoother clinical translation of these technologies. Nurses who proactively engage with this learning curve position themselves as valuable contributors to quality improvement initiatives and technology adoption committees within their GI units.

Bottom Line

As AI tools move from technical development into routine clinical use for polyp detection, documentation, and workflow management, GI nurses should prioritize building familiarity with these systems now, since their evolving role in the procedure room will increasingly require confidence in supporting, interpreting, and troubleshooting AI-assisted technology alongside physicians.