Don’t predict if you cannot interpret: investigating the clinical viability of facial movements for machine-learning assisted diagnostics of bipolar disorder

Our exploratory study argues for the necessity of devising inherently interpretable machine learning models for the clinical domain. Furthermore, we critically discuss the implications of identifying AU4 as a key discriminative feature and assess the clinical value of specific facial movements for the diagnostic process.