Endoscopic surgery, which was first introduced in the late 1980s, has rapidly become widespread. However, despite its popularity, the occurrence of intraoperative organ damage has not necessarily decreased. To avoid intraoperative bile duct injury in laparoscopic cholecystectomy, which is one of the most popular procedures in endoscopic surgery, we are developing a laparoscopic surgical system that uses Artificial Intelligence (AI) to identify four anatomical landmarks (cystic duct of the gallbladder, common bile duct, lower surface of hepatic S4, and Rouviere's sulcus, related to "Calot's triangle") in real time during surgery. The development process consists of 5 steps: 1) identification of anatomical landmarks, 2) collection and creation of teaching data, 3) annotation and deep learning, 4) validation of development model, and 5) actual clinical performance evaluation. At present, anatomical landmarks can be identified with high accuracy in an actual clinical performance test in laparoscopic cholecystectomy, whereas issues for practical clinical use, such as a need to recognize the scene of surgical steps and surgical difficulties related to inflammation of the gallbladder, have also been clarified. The development of an AI-navigation system for endoscopic surgery, which could identify anatomical landmarks in real time during surgery, could be expected to support surgeons' decisions, reduce surgical complications, and contribute to improving the quality of surgical treatments.