Mental Health
Mental health research increasingly leverages artificial intelligence, particularly large language models (LLMs) and multimodal machine learning, to improve diagnosis, assessment, and treatment. Current efforts focus on developing AI systems capable of analyzing diverse data modalities (text, speech, images) to detect and classify mental health conditions, predict severity, and provide personalized support, often employing techniques like chain-of-thought prompting and knowledge distillation. These advancements hold significant promise for enhancing accessibility, efficiency, and accuracy in mental healthcare, though challenges remain regarding data bias, model interpretability, and ethical considerations.
Papers
Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance
Abdelrahman A. Ali, Aya E. Fouda, Radwa J. Hanafy, Mohammed E. Fouda
Exploring Complex Mental Health Symptoms via Classifying Social Media Data with Explainable LLMs
Kexin Chen, Noelle Lim, Claire Lee, Michael Guerzhoy
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi