Editorial Commentary: Predicting Satisfaction After Hip Arthroscopy Using Machine Learning: What Do Treadmills and Black Boxes Have to Do With Arthroscopy?

Abstract

The use of advanced statistical methods and artificial intelligence including machine learning enables researchers to identify preoperative characteristics predictive of patients achieving minimal clinically important differences in health outcomes after interventions including surgery. Machine learning uses algorithms to recognize patterns in data sets to predict outcomes. The advantages are the ability, using "big data" registries, to infer relations that otherwise would not be readily understood and the ability to continuously improve the model as new data are added. However, machine learning has limitations. Models are only as good as the data incorporated, and data may be misapplied owing to huge data sets and strong computing capabilities, in which spurious correlations may be suggested based on significant P values. Hence, common sense must be applied. The future of outcome prediction studies will most definitely rely on machine learning and artificial intelligence methods.