Enhancing colonoscopy outcomes: A systematic review and meta-analysis of randomized controlled trials evaluating artificial intelligence–assisted adenoma and polyp detection.

Key Takeaways for GI Nurses

• Artificial intelligence (AI) technology is being systematically evaluated to improve adenoma and polyp detection rates during colonoscopy procedures, potentially enhancing colorectal cancer prevention efforts
• This systematic review and meta-analysis represents high-level evidence examining randomized controlled trials, providing valuable insights into AI-assisted endoscopy effectiveness
• Understanding AI integration in colonoscopy may become essential for endoscopy nurses as these technologies are increasingly adopted in clinical practice
• Enhanced detection capabilities could impact procedure workflows, documentation requirements, and patient education protocols in GI units

Clinical Relevance

The integration of artificial intelligence in colonoscopy represents a significant advancement in colorectal cancer screening and surveillance that will directly impact endoscopy nursing practice. As AI-assisted detection systems become more prevalent, GI nurses will need to understand how these technologies function alongside traditional endoscopic techniques. This may involve learning new equipment protocols, understanding AI alert systems, and adapting to modified procedure workflows when AI assistance is utilized during colonoscopy.

From a patient care perspective, improved adenoma and polyp detection rates through AI assistance could lead to better clinical outcomes and enhanced preventive care. Endoscopy nurses play a crucial role in patient education and may need to explain AI technology benefits to patients while addressing any concerns about computer-assisted diagnosis. Additionally, enhanced detection capabilities may result in longer procedure times, increased biopsy sampling, or changes in post-procedure patient monitoring requirements.

The systematic review methodology examining randomized controlled trials provides robust evidence that can guide evidence-based practice decisions in endoscopy units. As healthcare systems evaluate AI technology adoption, GI nurses will be instrumental in implementation planning, staff training, and quality assurance protocols. Understanding the evidence base for AI-assisted colonoscopy will enable nurses to advocate for appropriate technology integration while maintaining high standards of patient safety and care quality.

Bottom Line

This systematic review examining AI-assisted adenoma and polyp detection in colonoscopy provides important evidence for a technology that may soon transform endoscopy practice, requiring GI nurses to understand both the clinical benefits and practical implications of computer-assisted detection systems to maintain excellence in patient care and procedural outcomes as these innovations are integrated into routine colonoscopy screening and surveillance programs.