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Updated: Nov 1, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Striving for quality improvement: can artificial intelligence help?

P Sinonquel¹, R Bisschops¹

¹Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.

Best Practice & Research. Clinical Gastroenterology

|June 26, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances gastrointestinal (GI) endoscopy by improving polyp and neoplasia detection. AI-augmented endoscopy shows promise for better patient care and diagnostic accuracy, but requires further validation.

Keywords:

Artificial intelligence Computer-aided detection Computer-aided diagnosis Quality Upper and lower gastrointestinal tract

Related Experiment Videos

Last Updated: Nov 1, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Medical Technology
Artificial Intelligence
Gastroenterology

Background:

Gastrointestinal (GI) endoscopy quality is crucial for patient care.
Human limitations in endoscopy can impact diagnostic accuracy.
Artificial intelligence (AI) offers potential solutions to enhance endoscopy.

Purpose of the Study:

To review the current applications and potential of AI in GI endoscopy.
To highlight AI's role in improving detection, diagnosis, and quality standards.
To discuss the future integration of AI into clinical practice.

Main Methods:

Review of AI applications in upper GI endoscopy, capsule endoscopy, and colonoscopy.
Analysis of studies demonstrating AI's impact on neoplasia and polyp detection.
Consideration of AI's role in training and therapeutic decision-making.

Main Results:

AI demonstrates potential in detecting and characterizing upper GI neoplasia (Barrett's, squamous cell, gastric carcinoma).
Deep learning enhances pathology detection and reduces reading time in small bowel capsule endoscopy.
AI consistently improves polyp and adenoma detection rates in colonoscopy, a key quality indicator.

Conclusions:

AI-augmented endoscopy shows significant promise for improving diagnostic performance and patient care.
Further large-scale, multicenter validation trials are necessary for routine clinical integration.
AI has potential roles in quality improvement, training, and therapeutic decision support in endoscopy.