Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions

Artificial intelligence (AI) is revolutionizing the field of gastrointestinal (GI) endoscopy, a technology that relies heavily on images and optical data. Precancerous lesions and early cancers of the GI tract can be subtle and easily missed even on high-definition endoscopy and chromoendoscopy. The advancements in machine learning and deep learning led to the development of computer-aided models of high performance in image analysis. The convolutional neural networks of these models are trained to analyze large datasets of endoscopic images through the supervised learning approach. Their utilization enhances lesion detection and visibility. This aids in real-time classification and risk stratification of GI luminal lesions, thus assisting endoscopists in making more accurate and timely decisions. AI has shown promising results in the detection and characterization of premalignant and early malignant lesions of the GI tract, such as Barrett's esophagus, gastric atrophy, intestinal metaplasia, small bowel and colonic polyps, as well as early esophageal, gastric and colon cancers. This positive impact of AI is more established in the esophagus and stomach than in the colon. However, the impact of AI on patients' outcomes such as mortality and interval cancer incidence remains to be seen. This review highlights the breakthroughs and clinical applications of AI in the detection and characterization of premalignant lesions and early cancers of the GI tract.