A Multimodal AI-Powered System for Mental Health Diagnostics

Traditional ways of diagnosing mental health issues mainly rely on interviews with patients. These methods can be influenced by personal opinions and are difficult to use on a large scale. This paper presents a system that uses AI to offer an objective and data-driven approach for mental health assessments. It analyzes different types of data collected during patient interviews. The system uses a deep learning setup with three parts to process video, audio, and text all at once. It looks for small but meaningful signals in how people behave, speak, and write that are linked to mental health problems like depression and PTSD. The system then uses a special method that focuses on important features to predict the condition and estimate how severe it is. This method tries to overcome the problems of traditional methods by making diagnoses more accurate, consistent, and efficient. By providing strong evidence, the system helps doctors make better decisions, supporting their judgment and improving patient care.