Paper Title
AI-Powered Multi-Modal Diagnostic Tool for Early Detection of Depression

Abstract
Depression, anxiety, and apathy are common in older adults with cognitive decline, making early detection essential for timely intervention. While diagnosis traditionally relies on subjective assessments, advances in artificial intelli- gence (AI) and machine learning are enabling more objective, scalable,andreliableapproaches.Recentstudiesdemonstratethe effectivenessoflargelanguagemodels,multimodallearningfrom text, speech, and facial expressions, as well as physiological and behavioralsignalssuchasEEG,eyemovement,andgaitanalysis, in predicting depression severity and progression. Systematic reviews of hundreds of studies reveal the growing use of graph neuralnetworksforbrainconnectivity,largelanguagemodelsfor linguistic data, and deep learning for multimodal fusion, withAI-assisted methods consistently outperforming single-modality approaches.Despitetheseadvances,challengesremainregarding transparency,reproducibility,andstandardizationofmodelsand datasets.Collectively,theevidenceunderscoresthepromiseofAI- driven approaches in enhancing depression detection, providinga roadmap for future innovation in computational psychiatry. Keywords - Depression Detection, Major Depressive Disorder, Artificial Intelligence in Healthcare, Multimodal Analysis, Nat- ural Language Processing, Speech Analysis, Facial Expression Recognition, Physiological Biomarkers, Machine Learning, Ex- plainable AI