AI and Mental Health: Leveraging Machine Learning for Early Diagnosis and Intervention
Abstract
Mental health disorders are a growing global concern, yet early diagnosis remains challenging due to subjective assessment methods. This paper investigates the role of AI in mental health by leveraging machine learning techniques to analyze speech patterns, facial expressions, and behavioral data for early detection of conditions like depression and anxiety. We propose a deep learning model trained on multi-modal data to enhance diagnostic accuracy. Experimental results demonstrate improved detection rates compared to traditional assessment methods. Ethical considerations, including bias in AI models, data privacy, and the need for human oversight, are discussed.
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