Colonoscopic removal of adenomatous polyps reduce both the incidence and mortality of colorectal cancer (CRC). The common clinical management of colorectal polyp detected during colonoscopy is to remove them and send for histopathology to determine the subsequent surveillance interval. More than 80% of polyps detected during screening or surveillance colonoscopy are diminutive (≤5mm). As the chance of diminutive polyps to harbor cancer or advanced neoplasia is low, leave-in-situ and resect-and-discard strategies using optical diagnosis are recommended for non-neoplastic polyps by the American Society for Gastrointestinal Endoscopy (ASGE) and the European Society for Gastrointestinal Endoscopy (ESGE) so as to reduce the financial burden of polypectomy and histopathology. The societies proposed leave-in-situ strategy if optical diagnosis can achieve a negative predictive value (NPV) of \>90% for rectosigmoid polyp and resect-and-discard if an agreement of more than 90% concordance with histopathology-based post-polypectomy surveillance interval can be achieved. However, optical diagnosis is operator dependent and most endoscopists are reluctant to adopt this strategy in routine practice because of the need of strict training and auditing and fear of incorrect diagnosis. In the past decade, with the exponential increase in computational power, reduced cost of data storage, improved algorithmic sophistication, and increased availability of electronic health data, artificial intelligence (AI) assisted technologies were widely adopted in various healthcare settings to improve clinical outcomes, especially the quality of colonoscopy in the area of gastroenterology. Real time use of computer-aided diagnosis (CADx) for adenoma using AI systems were developed and proven to be useful to help endoscopists to distinguish neoplastic polyps from non-adenomatous polyps. However, these studies only examined diminutive polyp but not polyp of larger size (\>5mm). They were conducted with small sample size of less than few hundred subjects and the study settings were open-label and non-randomized. The investigators aim to conduct a large scale randomized controlled trial to evaluate the performance of colorectal polyp characterization of all size polyps by real-time CADx using AI system against conventional colonoscopy with optical diagnosis.