Editorial Commentary: Knowledge is Power: A Primer for Machine Learning

Abstract

Machine learning (ML) has become an increasingly common statistical methodology in medical research. In recent years, ML techniques have been used with greater frequency to evaluate orthopaedic data. ML allows for the creation of adaptive predictive models that can be applied to clinical patient outcomes. However, ML models for predicting clinical or safety outcomes may be made available online so that physicians may apply these models to their patients to make predictions. If the algorithms have not been externally validated, then the models are not likely to generalize, and their predictions will suffer from inaccuracy. This is especially important to bear in mind because the recent increase in ML papers in the medical literature includes publications with fundamental flaws.