Artificial Intelligence in Colonoscopy Surveillance for Lynch Syndrome: Emerging Evidence, Lessons Learned From Average-Risk Populations, and Future Directions.

Key Takeaways for GI Nurses

• Artificial intelligence technology is being evaluated specifically for Lynch syndrome patients during colonoscopy surveillance, potentially enhancing polyp detection in this high-risk population
• Evidence from average-risk colonoscopy populations provides valuable insights that can inform AI implementation strategies for Lynch syndrome surveillance protocols
• AI-assisted colonoscopy may require modifications to current nursing workflows, documentation practices, and patient education approaches for Lynch syndrome surveillance
• Understanding AI capabilities and limitations will become essential for nurses caring for hereditary cancer syndrome patients undergoing enhanced surveillance

Clinical Relevance

Lynch syndrome patients represent one of our most challenging surveillance populations, requiring frequent colonoscopies starting at young ages with meticulous attention to adenoma detection. The integration of AI technology into their surveillance protocols has significant implications for nursing practice. As AI systems are designed to assist endoscopists in real-time polyp detection, nurses must understand how these tools function within the procedure workflow, including potential alerts, system responses, and documentation requirements that may differ from standard colonoscopy protocols.

From a patient care perspective, Lynch syndrome patients often experience heightened anxiety about their cancer risk and may have questions about how AI technology affects their surveillance quality and outcomes. Nurses play a crucial role in patient education, explaining how AI enhancement works during colonoscopy and addressing concerns about technology reliability. Additionally, the specialized nature of Lynch syndrome surveillance means that AI implementation may require updated competency assessments, modified pre-procedure checklists, and enhanced communication protocols between nursing staff and endoscopists when AI alerts or recommendations occur during procedures.

The operational impact extends to unit workflow management, as AI-assisted procedures may have different timing considerations, equipment requirements, or post-procedure documentation needs. Nurses must also stay informed about the evidence base supporting AI use in high-risk populations, as this knowledge directly impacts patient advocacy and informed consent processes. Understanding the distinctions between AI performance in average-risk versus Lynch syndrome populations will be essential for providing accurate patient education and supporting clinical decision-making.

Bottom Line

As artificial intelligence becomes integrated into Lynch syndrome colonoscopy surveillance, GI nurses must prepare for evolving roles in patient education, procedural workflow management, and technology-enhanced care delivery. While AI shows promise for improving polyp detection in this high-risk population, nurses will remain essential for translating complex technology into compassionate, individualized patient care and ensuring that surveillance protocols meet the unique needs of hereditary cancer syndrome patients.