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Updated: Jan 18, 2026

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

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Published on: July 11, 2025

Artificial Intelligence in Upper Gastrointestinal Diagnosis.

Sabrina Xin Zi Quek¹, Khek Yu Ho^1,2

¹Division of Gastroenterology and Hepatology, Department of Medicine, National University of Singapore, Singapore.

The Korean Journal of Helicobacter and Upper Gastrointestinal Research

|September 11, 2025

Summary

Artificial intelligence (AI) enhances upper gastrointestinal endoscopy by improving disease detection and streamlining workflows. AI tools aid in diagnosing precancerous conditions and guiding early gastric cancer therapy, optimizing patient care.

Keywords:

Artificial intelligence Endoscopy Gastroscopy

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) is transforming upper gastrointestinal (GI) endoscopy.
AI technologies like machine learning and deep learning enhance diagnostic accuracy and clinical workflows.

Purpose of the Study:

To review the role of AI in upper GI endoscopy.
To explore AI applications in disease detection, characterization, and management.
To discuss challenges and future directions for AI in endoscopy.

Main Methods:

Review of AI technologies including machine learning, deep learning, and convolutional neural networks.
Exploration of AI applications in detecting lesions, precancerous conditions (Barrett's esophagus, atrophic gastritis, gastric intestinal metaplasia), and early gastric cancer.
Discussion of non-image-based AI tools like Raman spectroscopy.

Main Results:

AI systems demonstrate potential in identifying subtle lesions, assessing tumor margins, and reducing interobserver variability.
AI provides real-time decision support, minimizing biopsies and improving patient outcomes.
AI aids in detecting precancerous conditions and guiding therapy for early gastric cancer.

Conclusions:

AI integration in endoscopy faces challenges including validation, user-centric design, and training.
Balancing AI with clinical expertise is crucial to avoid overreliance and deskilling.
Future AI integration promises comprehensive diagnosis and personalized therapeutic planning in upper GI care.