The Role of Artificial Intelligence in Early Detection and Management of Anxiety and Depression Disorders

The rise in the cases of anxiety and depression kinds disorders has created a need amongst the humanities of trying to find new methods of diagnosing and acting in time. The paper addresses how they can be identified and solved by means of artificial intelligence (AI) by quantitatively analyzing the multimodal data (text, speech and physiological measures). The comparative performance of various models was made in relation to logistic regression, random forest, support vector machine, convolutional neural networks (CNN), long short-term memory network (LSTM) models as well as transformer-based models such as BERT and Roberta. The results showed that models that are based on transformers performed favorably over older machine learning frameworks as well as past deep learning framework, with robearta performing best in terms of accuracy (0.93) and AUC-ROC (0.96). The multimodal fusion of diagnostic reliabilities compared to single-modality certainly amplified the diagnostic reliability improvement therefore demonstrating the importance of combining various data models. The study has revealed that AI has the potential to be a powerful tool of early detection of anxiety and depression and hence timely responses and clinical judgment. However, the fact that there were some limitations, in particular, the heterogeneity of the data set, the issue of interpretation, and the ethical aspect plays the largest role in the center of interest of the future studies. Overall, the findings are that AI-based solutions, in particular, with multimodal and explainable approaches to mental health care are overly promising.