Integration of Artificial Intelligence-Enhanced Capsule Endoscopy in Clinical Practice: A Review of Market-Available Tools for Clinical Practice
A. Giordano, Cristina Romero-Mascarell, Begoña González-Suárez, Carlos Guarner‐Argente
Abstract
The integration of artificial intelligence (AI) into capsule endoscopy is transforming gastrointestinal diagnostics by enhancing lesion detection and optimizing reading efficiency. This review focuses on the clinical applications of commercially available AI-powered capsule endoscopy systems, particularly for small bowel evaluation. Recent clinical trials and observational studies are analyzed to assess the diagnostic performance, practical benefits, and limitations of these systems. Additionally, key challenges related to standardization, data quality, and clinical validation are discussed. Currently available AI systems significantly reduce reading times and demonstrate high detection capabilities, depending on the algorithm and device used. However, a substantial number of lesions remain undetected, preventing full reliance on these tools. Future advancements must focus on improving detection rates and validating the clinical relevance of missed lesions. Additionally, standardizing AI algorithms across different capsule systems is essential to ensure consistency, reliability, and broader clinical adoption. Establishing homologation frameworks will be key to achieving uniform performance and seamless integration into routine practice.