Quality control can decrease variations in the performance of colonoscopists and improve the effectiveness of colonoscopy to prevent colorectal cancers. Unfortunately, routine quality control is difficult to carry out because a practical method is lacking. The aim of this study was to develop an automatic quality control system (AQCS) and assess whether it could improve polyp and adenoma detection in clinical practice. First, we developed AQCS based on deep convolutional neural network models for timing of the withdrawal phase, supervising withdrawal stability, evaluating bowel preparation, and detecting colorectal polyps. Next, consecutive patients were prospectively randomized to undergo routine colonoscopies with or without the assistance of AQCS.