Machine learning is likely to become one of the most valuable tools for predicting outcomes in patients with patellofemoral instability. Traditional statistical analysis is challenging in this diagnosis as the result of the multitude of risk factors. However, 3 important cautions must be considered. (1) Machine learning is limited by the quality of the data entered. Many of the risk factors for patellofemoral instability rely on classification systems with significant interexaminer variability and patient-reported outcomes used to track changes contain inherent biases, especially with regard to race and gender. Poor data quality will lead to unreliable predictions, or "garbage in equals garbage out." (2) The optimal machine-learning algorithms for addressing specific clinical questions remain uncertain. (3) The question of how much data we really need for accurate analysis is unresolved, which again, is completely dependent on the quality of the data. Machine learning is the future; just beware of what goes into the chicken salad.
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