Machine learning for antidepressant treatment selection in depression

•The state-of-the-art in machine learning (ML) application in the selection of antidepressants for personalized treatment of major depressive disorder is presented.•ML is a promising tool to personalize antidepressant prescription.•The value of ML in the selection of antidepressants for clinical practice needs more research.•Other factors should be taken into account to make ML-based prediction models more useful for clinical application. Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.