With more than 500,000 surgeries performed daily worldwide, there is no shortage of data points to input, measure, analyze, and extrapolate. According to a study in the journal Artificial Intelligence in Medicine, those data may be used to accurately predict a surgeon’s next actions.

The development of context-aware systems is one of the ways artificial intelligence (AI) is making its presence known in everyday medical practice. These systems continuously monitor certain activities to provide accurate and reliable support functions. The main challenge is how to turn data points into useful information that can augment a surgeon’s decision-making abilities.

The field of Surgical Process Modeling has recently entered the operating room (OR) to help gain a more complete understanding of the description of surgeries through quantitative analysis of OR activities. However, this field is fraught with obstacles due to the data’s complexity. Two surgeons performing the same surgery who achieve similar outcomes may use different actions or techniques.

A study led by Germain Forestier, PhD, University of Haute-Alsace, Mulhouse, France, sought to tackle the potential for prediction using low-level data (eg, data points such as cut and suture) to extract higher-level knowledge.