Toward personalized classification and treatment in depression: A narrative review of digital phenotyping and artificial intelligence

Major depressive disorder (MDD) remains a highly heterogeneous condition, presenting significant challenges for effective diagnosis and treatment. Traditional diagnostic systems often fail to capture the diverse clinical and biological phenotypes of MDD, limiting the efficacy and predictability of therapeutic interventions. The advent of wearable technology has enabled the continuous collection of real‐time, objective data. By leveraging advanced artificial intelligence (AI) methodologies, these data streams can be transformed into dynamic digital phenotypes that may correlate with the complex psychopathological manifestations of depression. This integration offers a novel, data‐driven approach to augment traditional subjective assessments, paving the way for more precise classification and personalized treatment strategies. This review explores the potential of AI‐enhanced digital phenotyping to revolutionize depression diagnosis and management, advocating for a paradigm shift toward a more personalized, precision‐based approach in psychiatric practice.