The field of mental health support is witnessing a significant shift with the integration of Large Language Models (LLMs) as co-creators rather than mere assistive tools. Researchers are exploring innovative approaches to harness the potential of LLMs in enhancing accessibility, personalization, and crisis intervention. A key direction in this field is the development of structured pathways for the ethical and responsible deployment of LLMs, ensuring they align with clinical and ethical standards. Another area of focus is the evaluation of LLM-driven mental health support, with a emphasis on assessing trustworthiness, empathy, and cultural sensitivity. Furthermore, studies are investigating the impact of AI chatbots on psychosocial effects, including loneliness and socialization, and the need for responsible AI development to prevent manipulation and harm. Noteworthy papers in this area include:

• A position paper proposing the SAFE-i Guidelines and HAAS-e Framework for ethical and responsible LLM deployment, which provides a blueprint for data governance and human-centered assessment.
• A study on EFTeacher, an AI chatbot designed to generate Episodic Future Thinking cues, which highlights the potential of AI chatbots in behavior-oriented applications.
• A longitudinal randomized controlled study on the psychosocial effects of chatbot use, which underscores the complex interplay between chatbot design choices and user behaviors.
• A paper introducing the Human Notes Evaluator, an open-source tool for assessing clinical note quality and differentiating between human and AI authorship, which offers a valuable resource for researchers and healthcare professionals.