Artificial Intelligence in Depression Care: From Early Detection to Personalized Treatment and Future Directions

Abstract

Depression is a leading global mental health disorder affecting more than 280 million people worldwide, with conventional diagnostic and therapeutic approaches limited by subjectivity, delayed detection, and variability in treatment response. Artificial intelligence (AI) has emerged as a transformative approach enabling more objective, scalable, and data-driven depression care. This review synthesizes advances in AI for early detection, clinical diagnosis, and personalized treatment, examining machine learning, deep learning, and natural language processing applied to multimodal data including speech, facial expressions, social media behavior, wearable sensor data, and neuroimaging. It highlights AI-enabled interventions such as digital phenotyping, predictive modeling, and chatbot-assisted cognitive behavioral therapy. Despite promising outcomes, challenges persist including data privacy, algorithmic bias, and limited generalizability.