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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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An Esophagogastroduodenoscopy (EGD) is a diagnostic procedure in which an endoscopist uses a flexible, lighted endoscope to visualize the upper gastrointestinal (GI) tract. The procedure includes visualizing the oropharynx, esophagus, stomach, and the first part of the small intestine, the duodenum.
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Related Experiment Video

Updated: Jan 13, 2026

Murine Endoscopy for In Vivo Multimodal Imaging of Carcinogenesis and Assessment of Intestinal Wound Healing and Inflammation
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Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.

Xiaohan Cai1, Zexin Zhang1, Siqi Zhao1

  • 1Department of Gastroenterology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

Explainable AI (XAI) enhances trust in artificial intelligence (AI) for digestive endoscopy by making complex "black box" models interpretable. This review explores XAI

Keywords:
deep learningdigestive endoscopyexplainable artificial intelligencemachine learningmedical imagingvisual explanation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Artificial intelligence (AI) shows promise in analyzing digestive endoscopy images for gastrointestinal disease diagnosis.
  • Deep learning models, while powerful, often function as "black boxes," hindering clinical trust and adoption due to lack of interpretability.
  • Explainable AI (XAI) is crucial for enhancing transparency and building trust in AI-driven medical decision-making.

Purpose of the Study:

  • To systematically review the research progress and applications of Explainable AI (XAI) in digestive endoscopic image analysis.
  • To focus on visual explanation-based XAI methods and their role in enhancing transparency.
  • To identify current obstacles and future directions for XAI implementation in clinical practice.

Main Methods:

  • Systematic review of 34 articles across various digestive endoscopy modalities (esophagogastroduodenoscopy, colonoscopy, endoscopic ultrasonography, wireless capsule endoscopy).
  • Clarification of XAI definitions and mainstream classifications.
  • Introduction to principles and characteristics of visual explanation-based XAI methods.
  • Review of XAI applications in digestive endoscopy image analysis.

Main Results:

  • XAI methods, particularly visual explanation techniques, are increasingly applied to improve the interpretability of AI models in digestive endoscopy.
  • The review synthesizes current research, highlighting progress in understanding and applying XAI across different endoscopic procedures.
  • Identified challenges and future research avenues for trustworthy AI in digestive endoscopy.

Conclusions:

  • XAI provides a theoretical foundation for developing transparent and trustworthy AI-assisted diagnostic and treatment systems in digestive endoscopy.
  • Promoting the implementation and application of XAI is essential for broader clinical adoption of AI in gastroenterology.
  • Enhanced interpretability through XAI fosters crucial human-machine collaboration in endoscopic image analysis.