A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental
illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category.
In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next
step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention
to machine learning algorithms that can be used with the goal of handling multidimensional datasets.