Surgical Feedback

Surgical feedback research aims to improve surgical training and outcomes by automatically analyzing surgical procedures using computer vision and machine learning. Current efforts focus on developing multimodal models, incorporating audio and video data alongside textual information, often leveraging large language models and memory networks to understand surgeon intentions and provide real-time feedback on technical skills, anatomical accuracy, and procedural steps. This automated analysis has the potential to significantly enhance surgical training, improve surgical safety, and ultimately lead to better patient outcomes by providing objective, scalable assessment of surgical performance.