Aim The aim of this study was to evaluate if the application of Artificial Intelligence (AI) Colonoscopy CLN (ENDO‐AID) could increase the polyp detection rate (PDR). Methods and Materials A single center retrospective study was performed in Tin Shui Wai Hospital. PDR in CLN from 11/2020 to 03/2021 after the application of ENDO‐AID (AI group) was compared to the cases from 12/2019 to 11/2020 before the application of the practice (non‐AI group). Procedures were performed by a single endoscopist with experience in performing > 3,000 CLN. Variables, such as patients' demographic data, indications, incidence of PDR, Boston Bowel Preparation Scale BBPS, withdrawal time, post CLN complication rate between the 2 groups, were compared. Categorical and continuous variables were analyzed by using the Chi‐Square test (Fisher exact test if appropriate) and Mann‐Whitney test respectively. Results were considered to be significant if p‐value < 0.05. Results Total 234 patients were recruited. 115 patients (49.1%) were in the non‐AI group while 119 patients (50.9%) were in the AI group. The mean age of the non‐AI was higher than the AI group (65.3 vs 59.2, p< 0.001*), otherwise, there was no significant difference in sex (p = 0.05), percentage of smokers (20.8% vs 27.7%, p = 0.22), past medical history of IBD (0 vs 0, p = 1.0), family history of colorectal cancer (9 vs 9, p = 0.94), indications for CLN (e.g. follow up CLN for polyp/ cancer, per‐rectal bleeding, altered bowel habit etc. p > 0.05), BBPS (7.88 vs 8.04, p = 0.217), withdrawal time (7.65 min vs 7.48 min, p = 0.935), completion rate (95.6% vs 98.3%, p = 0.27) and complication rate (0% in both groups,p=1.0) between groups. In the contrary, PDR was significantly higher in the AI group than the non‐AI group (64.7% vs 46.0%, p = 0.003*). Besides, adenoma detection rate was also found significantly higher in the AI group than the non‐AI group (52.9% vs 37.4%, p = 0.017*). Conclusions AI CLN can improve the PDR.