Rui Liu, Jing Guo, Eben Sophia P
Depression and anxiety are among the most widespread mental health disorders, yet conventional diagnostic methods often face limitations due to subjectivity, stigma, and restricted access. This review presents a concise overview of recent advancements in artificial intelligence (AI) for the early detection, monitoring, and management of these conditions. We explore AI methodologies utilizing multimodal data (including text, audio, images, and behavioral patterns) from key sources, such as social media content, speech, facial expressions, and wearable sensors. The methodologies highlighted include natural language processing, sentiment analysis, machine learning classifiers, and deep learning models. Multimodal frameworks enhance diagnostic precision and enable real-time monitoring, and personalized care via AI-powered chatbots and wearable devices. AI offers transformative potential in mental healthcare by providing more objective, scalable, and continuous psychological assessment. However, challenges regarding ethical data handling, model transparency, and clinical validation must be addressed, requiring the development of ethical frameworks and regulatory standards. This review underscores AI’s role in enhancing early detection and individualized management, providing a comparative synthesis of datasets and evaluation practices, and highlighting cross-cultural and benchmarking gaps that currently limit clinical translation.