Abstract: With the advancements in artificial intelligence (AI), computational power, and surgical robotics, the analysis of surgical performance at the granular level of individual surgical gestures has become feasible. Surgical gestures, defined as the smallest independent units of inter-action between surgical instruments and tissues, offer a quantifiable framework for surgical skill assessment. Evidence suggests that the selection and execution of surgical gestures are strongly correlated with the expertise of the surgeon and patient outcomes, underscoring their significance in both surgical training and clinical practice. Moreover, the establishment of a standardized classifi-cation system for surgical gestures and the adoption of uniform terminology have the potential to improve communication efficiency during surgical education and training. The authors synthesize existing classification systems for surgical gestures, with a focus on their applications in diverse tasks such as suturing, exposure and dissection. By examining the latest advancements in AI models applied to surgical gesture, as well as the current research landscape of surgical gesture recognition in digestive surgery, the authors explore the potential applications of such technologies in assisting surgeons during operations in the future.