A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …

Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery
(RAMIS), has become the first domain within medicosurgical robotics that achieved a true …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes.
However, for most surgical procedures, the details of intraoperative surgical actions, which …

Although anaesthesiologists strive to avoid hypoxaemia during surgery, reliably predicting
future intraoperative hypoxaemia is not possible at present. Here, we report the …

Objective: To provide an overview of ML models and data streams utilized for automated
surgical phase recognition. Background: Phase recognition identifies different steps and …

Objective: To develop a deep learning model to automatically segment hepatocystic
anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic …

Objective: The aim of this study was to develop and evaluate the performance of artificial
intelligence (AI) models that can identify safe and dangerous zones of dissection, and …

Accurate and objective performance assessment is essential for both trainees and certified
surgeons. However, existing methods can be time consuming, labor intensive, and subject …