RESUMEN
Objectives This review is motivated by the observation that clinical decision-making in mental health is limited by the nature of the measures obtained in conventional clinical interviews and the difficulty for clinicians to make accurate predictions about their patients' future mental states. Our objective is to offer a representative overview of the potential of digital phenotyping coupled with machine learning to address this limitation, while highlighting its own current weaknesses. Methods Through a non-systematic narrative review of the literature, we identify the technological developments that make it possible to quantify, moment by moment and in ecologically valid settings, the human phenotype in various psychiatric populations using the smartphone. Relevant work is also selected in order to determine the usefulness and limitations of machine learning to guide predictions and clinical decision-making. Finally, the literature is explored to assess current barriers to the adoption of such tools. Results Although emerging from a recent field of research, a large body of work already highlights the value of measurements extracted from smartphone sensors in characterizing the human phenotype in behavioral, cognitive, emotional and social spheres that are all impacted by mental disorders. Machine learning permits useful and accurate clinical predictions based on such measures, but suffers from a lack of interpretability that will hamper its use in clinical practice in the near future. Moreover, several barriers identified both on the patient and clinician sides currently hamper the adoption of this type of monitoring and clinical decision support tools. Conclusion Digital phenotyping coupled with machine learning shows great promise for improving clinical practice in mental health. However, the youth of these new technological tools requires a necessary maturation process to be guided by the various concerned actors so that these promises can be fully realized.