Mental Health Support System: A Comprehensive Survey

This paper presents a comprehensive study on the fine-tuning and application of AI-driven Large Language Models (LLMs) for mental health support systems. Specifically, the study focuses on systems designed to address mild mental health challenges such as stress, anxiety, depression, and loneliness. By leveraging pre-existing technologies such as Transformers, Retrieval Augmented Generation (RAG), Langchain, prompt engineering, and agent-based approaches, these systems offer scalable, personalized, and accessible mental health care. The paper also examines the current global and Indian mental health landscape, including the prevalence of mental health issues, the shortage of mental health professionals, and the growing demand for scalable solutions. Key challenges such as data privacy, high computational requirements, and ensuring continuous companion support are discussed, alongside the ethical considerations in AI-driven mental health care. Finally, future directions for improving LLM-based mental health systems are explored, focusing on advancements in RAG, intent recognition, personalization, and integration with traditional healthcare systems.