Personalized mental health: Artificial intelligence technologies for treatment response prediction in anxiety disorders

Abstract Anxiety disorders pose an enormous burden on affected individuals and societies. Evidence-based psychotherapeutic and pharmacological treatments exist; yet, not all patients respond equally well. The emerging paradigm of personalized medicine in mental health aims to offer solutions for those vulnerable patient groups by better matching patient characteristics to custom-tailored treatment approaches. Novel technologies based on machine learning and artificial intelligence allow for predictions on the individual patient level—a necessary prerequisite for personalizing treatments. This chapter will outline the current state of evidence regarding predictive biomarkers and mechanisms of treatment (non)response in anxiety disordered patients, followed by an executive summary on the current status quo and future challenges in the field of predictive analytics. Artificial intelligence in mental health may bear the potential to foster clinical applicability of neuroscience-informed research and to bridge the translational gap between clinical research and practice.