Depression Detection and Diagnosis: Using an AI Perspective

Depression is a widespread and complex mental health disorder requiring accurate and timely diagnosis. This review explores the potential of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in enhancing depression detection through diverse data sources such as EEG, fMRI, audio, and text. Models like SVM, CNN, LSTM, and BERT, combined with hybrid approaches, have demonstrated significant accuracy, especially when used with preprocessing techniques and explainable AI tools like SHAP and LIME. The integration of linguistic, behavioral, and neurophysiological data improves early diagnosis and supports clinical outcomes. However, challenges such as data heterogeneity, limited sample sizes, and generalizability issues persist. Future research should prioritize the development of scalable and interpretable systems to aid healthcare professionals in delivering personalized care.