Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

A systematic review from arXiv (paper 2608.18080v1) examines large language model (LLM) applications in mental health, covering social media analysis, clinical conversational agents, therapy support, and multimodal learning. The review highlights advances in early depression detection, suicide risk assessment, and personalized therapy, while emphasizing prompt engineering and ethical, regulatory challenges for safe deployment.

arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models LLMs in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records, and multimodal inputs to enable early detection of depression, suicide risk assessment, personalized therapy support, and psychoeducational content generation. Our review highlights advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance, while we also emphasize the critical role of prompt engineering for domain adaptation. We also discuss emerging multimodal fusion techniques integrating text, speech, and sensor data for improved mental health diagnosis and monitoring. Finally, we address ongoing ethical, sociotechnical, and regulatory challenges, and advocate frameworks to ensure safe, equitable, and accountable deployment of LLMs in real-world mental health care.