AI-powered conversational framework for mental health diagnosis

In the domain of mental health care, conditions such as anxiety, depression, bipolar disorder, and borderline personality disorder (BPD) affect millions globally, often going undetected due to stigma, limited access to specialists, and the complexity of diagnosis. In this study, a hybrid AI framework is proposed that combines conversational intelligence with deep learning-based classification to assist in mental health screening. The system utilizes GPT-3.5 to conduct adaptive, human-like conversations for gathering user responses, which are then analyzed by a fine-tuned DistilRoBERTa model for accurate multi-class classification. Further, a strategic data sampling technique is employed using t-distributed stochastic neighbor embedding (t-SNE) and Sentence-Bidirectional Encoder Representations from Transformers (BERT) embeddings to select the most representative samples per class from a public Reddit mental health dataset. The classification model achieved high performance, with an accuracy of 96.27%, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC) scores consistently above 0.91 across all classes, indicating strong discriminative capability. The system is computationally efficient, with an average inference time of 1.67 milliseconds per sample, making it suitable for real-time applications. This work offers a lightweight, scalable, and explainable solution that can assist professionals or be integrated into virtual mental health assistants.