Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes

Area of Science:

• Gastroenterology
• Medical Technology
• Artificial Intelligence

Background:

• Colonoscopy is crucial for colorectal cancer prevention but relies on quality indicators.
• Maintaining consistent endoscopist performance and quality metrics remains a challenge.

Purpose of the Study:

• To review the role of AI in improving colonoscopy quality indicators.
• To explore AI's potential in reducing errors and enhancing standardization.

Main Methods:

• Review of AI-driven technologies (deep learning, computer vision, real-time feedback).
• Analysis of AI's impact on colonoscopy quality metrics and patient outcomes.

Main Results:

• AI can reduce human error and improve real-time decision-making for endoscopists.
• AI facilitates better lesion characterization, precise resection, and reduced interval cancers.

Conclusions:

• AI offers significant promise for advancing colonoscopy quality and patient care.
• Challenges like cost-effectiveness and training impact AI integration.