Stitching a patient back together after surgery is a vital but monotonous task for medics, often requiring them to repeat the same simple movements over and over hundreds of times. But thanks to a collaborative effort between Intel and the University of California, Berkeley, tomorrow's surgeons could offload that grunt work to robots -- like a macro, but for automated suturing.

The UC Berkeley team, led by Dr. Ajay Tanwani, has developed a semi-supervised AI deep-learning system, dubbed Motion2Vec. This system is designed to watch publically surgical videos performed by actual doctors, break down the medic's movements when suturing (needle insertion, extraction and hand-off) and then mimic them with a high degree of accuracy. 

"There's a lot of appeal in learning from visual observations, compared to traditional interfaces for learning in a static way or learning from [mimicking] trajectories, because of the huge amount of information content available in existing videos," Tanwani told Engadget. When it comes to teaching robots, a picture, apparently, is worth a thousand words.