The field of mental health diagnosis and support is undergoing significant transformations with the integration of artificial intelligence (AI) and machine learning (ML) technologies. Recent developments have focused on creating more accurate, explainable, and trustworthy diagnostic tools, as well as personalized support systems. Multi-agent frameworks and large language models (LLMs) are being explored for their potential to simulate clinical dialogues, generate diagnostic questionnaires, and provide empathetic support. Noteworthy papers include: Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis, which proposes a novel LLM-based agent workflow for autonomous generation of diagnostic questionnaires. AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment, which introduces an adaptive questioning mechanism to address ambiguity and missing information in mental health evaluations. LLM4Sweat: A Trustworthy Large Language Model for Hyperhidrosis Support, which presents a domain-specific LLM framework for trustworthy and empathetic support of individuals with hyperhidrosis. M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals, which introduces a novel dataset for detecting help-seeking behavior on social media.