A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.

Abstract

Endoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in the early detection of laryngopharyngeal lesions. However, UADT endoscopy remains operator-dependent and lacks standardized quality metrics. Recent advancements in artificial intelligence (AI) have generated interest in applications within UADT endoscopy. This review evaluates the clinical impact of AI in UADT endoscopy. A literature review was conducted up to December 31, 2024. Studies were evaluated using the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Eighty-three studies were included. Results indicate that AI in UADT endoscopy achieves diagnostic accuracy, sensitivity, and specificity rates comparable to experts, with optimal outcomes combined with human expertise. AI also demonstrated significantly faster inference times. This review highlights AI's potential to enhance clinical impact in UADT endoscopy, especially when combined with human expertise. However, the limited focus on real-time clinical translation underscores the need for further research to enable effective integration into clinical practice.