David-Olawade, Aanuoluwapo Clement, Aderinto, Nicholas, Egbon, Eghosasere, Olatunji, Gbolahan Deji, Kokori, Emmanuel and Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836 (2025) Enhancing Endoscopic Precision: The Role of Artificial Intelligence in Modern Gastroenterology. Journal of Gastrointestinal Surgery. p. 102195. (In Press)

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Abstract

Background

Endoscopy remains the gold standard for gastrointestinal diagnostics, enabling direct visualisation and intervention within the gastrointestinal (GI) tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on endoscopist skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision.


Aim

This review explores the current applications of AI in endoscopy, focusing on image analysis, lesion detection, classification, and workflow optimisation, whilst evaluating the impact on clinical practice and identifying implementation challenges.


Method

A literature search was conducted using PubMed, Google Scholar, and IEEE Xplore databases for studies published between January 2010 and December 2024. Keywords included "Artificial Intelligence," "Endoscopy," "Gastrointestinal Diseases," "Image Analysis," and "Lesion Detection." Studies were selected based on their focus on AI applications in endoscopy with quantitative or qualitative data on performance and clinical impact.


Results

AI demonstrates exceptional capabilities in polyp detection, achieving detection rates that often surpass human practitioners, with systems like GI Genius showing high sensitivity and specificity. Convolutional Neural Networks excel in real-time lesion identification and classification, differentiating between benign and malignant growths with remarkable precision. AI also optimises endoscopic workflows through automated reporting and advanced training tools.


Conclusion

While AI integration shows promise for enhancing endoscopic diagnostic accuracy and procedural efficiency, successful implementation requires careful consideration of current limitations, including reliance on industry-sponsored studies, and addressing challenges in data quality, clinical workflow integration, and regulatory considerations. Future developments in advanced algorithms, personalised medicine, and telemedicine may further advance endoscopic practice and improve patient outcomes.

Item Type:	Article
Status:	In Press
DOI:	10.1016/j.gassur.2025.102195
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12522

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