Multimodal Deep Learning Applied to the Analysis of Depression: The Present Trend, Challenges and Future Directions

A large number of people in the world face a massive global health crisis in the Major Depressive Disorder (MDD). Traditional methods of diagnosis are usually subjective and inaccessible. Multimodal deep learning is the integration of behavioral biomarkers of speech, text, and image in order to effectively diagnose depression. This is a detailed survey of the latest developments in multimodal depression recognition with attention paid to the feature extraction mechanisms and fusion structures. We address the key issues such as cultural biases, the lack of data, and the situation between detection and preventive care. The subsequent areas of research are culturally adaptive models, privacy-sensitive learning, explainable AI in clinical settings, and mobile-guided sensing to maintain mental health measurements. It has been evidenced that multimodal frameworks are able to enhance early risk stratification and fore- cast long-term symptom trajectory. Cross-cultural datasets, ethics and clinician-approved interpretability systems Multimodal AI systems can be used to increase the efficiency of screening, decrease diagnostic delays, and expand the ability to provide mental care to underserved populations.