Abstract
Artificial intelligence (AI) systems, especially after the successful application of Convolutional Neural Networks, are revolutionizing modern medicine. Gastrointestinal Endoscopy has shown to be a fertile terrain for the development of AI systems aiming to aid endoscopists in various aspects of their daily activity. Lesion detection can be one of the two main aspects in which AI can increase diagnostic yield and abilities of endoscopists. In colonoscopy, it is well known that a substantial rate of missed neoplasia is still present, representing the major cause of interval cancer. In addition, an extremely high variability in adenoma detection rate, the main key quality indicator in colonoscopy, has been extensively reported. The other domain in which AI is believed to have a considerable impact on everyday clinical practice is lesion characterization and aid in "optical diagnosis". By predicting in vivo histology, such pathology costs may be averted by the implementation of two separate but synergistic strategies, namely the "leave-in-situ" strategy for < 5 mm hyperplastic lesions in the rectosigmoid tract, and "resect and discard" for the other diminutive lesions. In this opinion review we present current available evidence regarding the role of AI in improving lesions' detection and characterization during colonoscopy.