Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update

Area of Science:

• Gastrointestinal Endoscopy
• Artificial Intelligence
• Medical Imaging

Background:

• Artificial intelligence (AI) is increasingly integrated into gastrointestinal (GI) endoscopy.
• AI applications span various endoscopic procedures, including colonoscopy, upper endoscopy, and capsule endoscopy.
• Key AI technologies include computer-assisted detection (CADe), diagnosis (CADx), and quality assessment (CAQ).

Purpose of the Study:

• To review the current state and impact of AI in gastrointestinal endoscopy.
• To highlight advancements in AI-driven tools for endoscopic procedures.
• To identify challenges and future directions for AI in this field.

Main Methods:

• Review of AI applications in colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy.
• Analysis of AI's role in lesion detection, procedural quality, workflow efficiency, and diagnostic support.
• Examination of current evidence, limitations, and future prospects of AI in GI endoscopy.

Main Results:

• AI systems show improvements in lesion detection, quality assessment, and workflow efficiency.
• AI enhances diagnostic support across diverse GI endoscopic modalities.
• Current evidence often relies on surrogate outcomes, with limited focus on patient-centered clinical benefits.

Conclusions:

• AI is transforming GI endoscopy towards standardization and data-driven practices.
• Challenges like generalizability, explainability, and regulatory oversight hinder widespread AI adoption.
• Future progress necessitates real-world validation, explainable AI, and seamless human-AI integration for improved patient outcomes.