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
The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support
Inhwa Song, Sachin R. Pendse, Neha Kumar, Munmun De Choudhury
Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda
Richard Kimera, Daniela N. Rim, Joseph Kirabira, Ubong Godwin Udomah, Heeyoul Choi
Challenges of Large Language Models for Mental Health Counseling
Neo Christopher Chung, George Dyer, Lennart Brocki
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression
Sumit Dalal, Deepa Tilwani, Kaushik Roy, Manas Gaur, Sarika Jain, Valerie Shalin, Amit Sheth