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

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... or prediction and used 6 machine-selected metrics. Three of the 4 algorithms used in the study misclassified a medical student as a neurosurgeon. The article addresses a very important question, using a valid approach, and presents credible and promising results. The authors' work prompts wider consideration of how to apply artificial intelligence to human behavior in medicine, particularly to the performance of technical tasks. The most fundamental of these applications is the question of meaning. Artificial intelligence, of which machine learning is one advanced application, refers to the capacity of a computer to perform operations analogous to learning and decision-making in humans. The objective of this study was "to identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure." 1 In the absence of a standard criterion or objective measure (such as time taken to complete a 100-m race), machine learning offers unprecedented capacity to identify associations between different variables (in many combinations or forms) in a particular system. To put these discoveries to use, it is necessary to understand the significance of key variables. In this case, does participant role or title equate to level of expertise? Is a neurosurgeon's performance invariably more "expert" than that of a fellow or resident? If it is not, then perhaps some of the prediction "errors" were not erroneous. Metric-based assessment of consultant surgical performance consistently identifies a significant minority of inferior performing outliers (>2 SD from the mean). 2 In this study, concurrent application of expertderived performance metrics 3,4 could have enabled discrimination between career stage and level of performance.