Multimodal Deep Learning for Early Detection of Depression and Anxiety through Explainable AI

Mental illness, particularly depression and anxiety, is a leading cause of global disease burden. Underdiagnosis is common due to misperceptions and negative stigma around mental health, limited resources, and self-reporting bias. Newer multimodal deep learning (MDL) frameworks have demonstrated the ability to distill behavioral, linguistic, and physiological signals pertaining to mental health from a number of data streams. However, uptake in clinical practice has been limited partly due to lack of transparency in how the models reach their conclusions. This study proposes a multimodal deep learning framework for the automatic early detection of anxiety and depression from text, audio and video signals with a special focus on Explainable AI(XAI). Basing the research on the benchmark datasets DAIC-WOZ, E-DAIC, and eRisk, the model outperformed unimodal baselines, and delivered clinically meaningful results that were interpretable. The research shows that leveraging explanatory artificial intelligence with MDL frameworks can create a more reliable and transparent AI-based screening tool for mental health problems.