VR and machine learning: novel pathways in surgical hands-on training

Domenico Venezianoa, Giovanni Cacciamanib, Juan Gomez Rivasc, Nicola Marinod, and Bhaskar K. Somanie

Curr Opin Urol 2020, 30:817–822


Purpose of review – Surgical training has dramatically changed over the last decade. It has become not only the way to prepare surgeons for their everyday work, but also a way to certify their skills thus increasing patient safety. This article reviews advances in the use of machine learning and artificial intelligence applied to virtual reality based surgical training over the last 5 years.

Recent findings – Eight articles have been published which met the inclusion criteria. This included six articles about the use of machine learning and artificial intelligence for assessment purposes and two articles about the possibility of teaching applications, including one review and one original research article. All the research articles pointed out the importance of machine learning and artificial intelligence for the stratification of trainees, based on their performance on basic tasks or procedures simulated in a virtual reality environment.

Summary – Machine learning and artificial intelligence are designed to analyse data and use them to take decisions that typically require human intelligence. Evidence in literature is still scarce about this technology applied to virtual reality and existing manuscripts are mainly focused on its potential to stratify surgical performance and provide synthetic feedbacks about it. In consideration of the exponential growth of computer calculation capabilities, it is possible to expect a parallel increase of research about this topic within the next few years.