Artificial intelligence applications for learning in robotic surgery

Abstract

Abstract Introduction Robot-assisted surgery (RAS) has become the standard surgical technique in several specialties due to its many advantages. Furthermore, artificial intelligence (AI) has seen an exponential increase in its applications over recent years, leading to the development of numerous algorithms designed to assist in decision support. The primary objective of this study is to evaluate the current applications of AI in the learning process of RAS. Methods An exhaustive search was conducted in various scientific databases, including PubMed, Scopus, and WoS. A set of studies that met the search criteria was selected, and from these, the key current applications of AI in learning in RAS were extracted. Results The key current applications of AI in learning in RAS include:Providing feedback to surgeons through algorithms that assist in improving the surgical procedure, offering virtual assistants with useful recommendations, and highlighting common errors.Enhancing the realism of simulation textures, thereby improving the quality of visual environments.Planning optimal trajectories for learning in laparoscopic activities, automating trajectory calculation from preoperative imaging studies.Adapting and optimizing the learning process by self-adjusting based on the surgeon’s progress, common errors, strengths, and weaknesses. Conclusion AI applications in learning for RAS remain an evolving research field, with significant potential for future advancements. The synergy between technical and clinical aspects offers exciting opportunities for enhancing surgical training.