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Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and
Ali Sahafi1, Anastasios Koulaouzidis2, Amin Naemi3
1Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 5230 Odense, Denmark.
Diagnostics (Basel, Switzerland)
|May 13, 2026
Summary
Artificial intelligence (AI) shows promise for analyzing wireless capsule endoscopy videos, improving diagnostic accuracy for gastrointestinal conditions. However, further research is needed to address validation and reporting inconsistencies for clinical adoption.
Area of Science:
- Gastroenterology
- Medical Imaging
- Artificial Intelligence
Background:
- Manual interpretation of wireless capsule endoscopy (WCE) videos is time-consuming and subjective.
- Artificial intelligence (AI) is being explored to aid WCE analysis and reduce clinician workload.
- This review synthesizes evidence on AI in WCE, focusing on diagnostic performance, validation, and clinical readiness.
Purpose of the Study:
- To systematically review and meta-analyze AI methods applied to WCE.
- To evaluate the diagnostic performance of AI in WCE across various gastrointestinal conditions.
- To identify barriers to AI adoption in WCE and propose recommendations for future research.
Main Methods:
- Systematic literature search across major scientific databases (PubMed, Scopus, Embase, Web of Science, Google Scholar).
- Inclusion of original journal articles meeting predefined eligibility criteria.
- Meta-analysis of diagnostic performance using random effects models for AI tasks in WCE.
Main Results:
- 72 studies were included, demonstrating high pooled diagnostic performance for AI in WCE, particularly for bleeding and vascular lesions.
- Variable performance was noted for inflammatory bowel disease and mixed abnormality categories.
- Key barriers identified include limited external validation, small cohorts, retrospective designs, and inconsistent reporting.
Conclusions:
- AI holds significant potential to enhance WCE interpretation and support clinical workflows.
- Recommendations are proposed to improve study design and validation for more robust AI results.
- Addressing identified barriers is crucial for the reliable translation of AI into clinical practice.
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