Artificial intelligence-assisted endoscopy in the detection of early gastrointestinal cancer: Progress, challenges, and future directions

Abstract

Gastrointestinal (GI) cancers are a leading cause of cancer-related death, and early diagnosis is crucial for improving patient outcomes. Traditional endoscopy, while essential, depends on the skill of endoscopists and is prone to errors. Recent advancements in artificial intelligence (AI), particularly deep learning with convolutional neural networks, have shown promise in enhancing the early detection of GI cancers. This review highlights the role of AI-assisted endoscopic technologies in the detection, localization, and diagnosis of GI cancers across various sites, including the oral cavity, pharynx, esophagus, stomach, small intestine, colon, and anal canal. AI-powered systems, such as computer-aided detection and diagnosis, have significantly improved adenoma detection rates and lesion characterization, aiding clinical decision-making. Integrating AI with advanced endoscopic techniques like narrow-band imaging, magnifying endoscopy, and capsule endoscopy has enhanced diagnostic accuracy. Despite these advances, challenges remain, including model generalization, data quality, and the need for efficient human-AI collaboration. Regulatory approval, legal concerns, and integration into clinical workflows also pose barriers to widespread adoption. Future developments in multimodal data fusion, edge computing, and AI-augmented reality are expected to improve the precision and accessibility of AI-assisted endoscopy for early GI cancer screening.

Keywords: Artificial intelligence; Gastrointestinal endoscopy; Early cancer detection; Deep learning; Computer-aided diagnosis

Core Tip: Artificial intelligence (AI)-assisted endoscopy technologies have significantly advanced early detection and diagnosis of gastrointestinal cancers, enhancing adenoma detection rates and improving clinical outcomes. Recent studies demonstrate that AI, through deep learning models, can effectively identify small lesions, reduce missed diagnoses, and assist in clinical decision-making across various gastrointestinal regions, including the esophagus, stomach, and colon. However, challenges such as data quality, model generalization, and physician-AI collaboration remain. Overcoming these issues will ensure AI’s broader clinical integration, making it a vital tool in precision medicine and early cancer screening.