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

Key Takeaways for GI Nurses

• Artificial intelligence (AI)-assisted polyp detection tools are increasingly integrated into endoscopy suites, meaning nurses will need to become familiar with how these systems flag lesions in real time during colonoscopy procedures.
• AI is expanding beyond detection alone into documentation support and workflow management, which may change how nurses assist with charting, procedure logs, and unit scheduling.
• Successful clinical translation of AI depends heavily on how well staff—including nurses—understand, trust, and incorporate these tools into existing workflows, not just on the technology's technical accuracy.
• Nurses are positioned as key partners in bridging the gap between AI's technical performance in trials and its practical, safe use in everyday endoscopy practice.

Clinical Relevance

The introduction of AI-assisted polyp detection systems directly affects the nursing role during colonoscopy procedures. As computer-aided detection (CADe) software highlights suspicious areas on the endoscopic display in real time, nurses assisting the endoscopist must understand what these visual cues mean, how they may influence procedural pacing, and how to support clear communication between the care team when AI flags a finding. This requires nurses to develop a working knowledge of AI capabilities and limitations so they can appropriately contextualize alerts for patients and help maintain procedural flow without over-relying on or dismissing software prompts.

Beyond detection, the expansion of AI into documentation and workflow management has practical implications for day-to-day unit operations. If AI tools assist with automated reporting, lesion characterization, or procedure logging, nursing documentation practices may need to adapt to incorporate or verify AI-generated data. Additionally, AI-driven workflow management—such as optimizing scheduling or monitoring procedure efficiency—could change how nurses coordinate patient flow, turnover times, and resource allocation within the endoscopy unit. Nurses who understand these systems can play a valuable role in identifying workflow bottlenecks or inefficiencies that AI tools are designed to address.

From a professional development standpoint, this shift underscores the growing need for GI nurses to engage in ongoing education about emerging technologies. As AI moves from technical validation studies into routine clinical use, nurses serve as frontline users who can provide essential feedback on usability, patient safety, and integration challenges. This positions nursing staff not merely as passive recipients of new technology, but as active contributors to refining how AI tools function safely and effectively within real-world GI practice settings.

Bottom Line

As AI tools move from research settings into everyday endoscopy practice, GI nurses must build familiarity with how these systems support polyp detection, documentation, and workflow—ensuring that technology enhances, rather than disrupts, safe and efficient patient care.