Artificial Intelligence for Detecting Mental Disorders: A Review

Abstract

Mental disorders demonstrate a significant global health challenge, affecting millions of individuals and often leading to severe social and economic consequences. Traditional diagnostic methods, such as clinical interviews and self-report questionnaires, are limited by subjectivity and scalability issues. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer promising solutions for early detection and accurate classification of mental health conditions. This review explores the AI techniques in diagnosing disorders such as depression, anxiety, bipolar disorder, schizophrenia, PTSD, and ADHD. It summarizes state-of-the-art methodologies, highlights publicly available datasets, and discusses key challenges, including data scarcity, model interpretability, and bias. The paper concludes by outlining future research directions focused on multimodal models, explainable AI, privacy-preserving techniques, and clinical integration to enhance mental health care.