Artificial intelligence and training physicians to perform technical procedures

Abstract

Winkler-Schwartz et al have set out to determine if some combination of machine learning algorithms can differentiate participants according to their stage of practice (ie, neurosurgeon, fellow, senior or junior resident, or medical student) based on their performance of a complex simulated neurosurgical task. A total of 250 simulated surgical resections performed by 50 participants were studied using a prospective, observational case series design. The best-performing algorithm (K-nearest neighbor) had 90% accuracy for prediction and used 6 machine-selected metrics. Three of the 4 algorithms used in the study misclassified a medical student as a neurosurgeon.