LLMs in Mental Health: Review Covers Applications, Innovations, Ethics

This comprehensive review examines the growing role of large language models (LLMs) within the mental health domain. It covers a wide array of applications, from analyzing social media posts to identifying early signs of depression and assessing suicide risk. The review also delves into the use of LLMs as clinical conversational agents, personalized therapy support tools, and generators of psychoeducational content. The paper integrates findings from various interdisciplinary studies, showcasing how diverse data sources, including electronic medical records and multimodal inputs, are being leveraged. It highlights significant advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance. A particular emphasis is placed on the critical role of prompt engineering in adapting LLMs for specific mental health contexts. Furthermore, the review discusses emerging multimodal fusion techniques that combine text, speech, and sensor data to improve mental health diagnosis and monitoring. Crucially, it addresses the ongoing ethical, sociotechnical, and regulatory challenges associated with deploying LLMs in real-world mental healthcare settings, advocating for robust frameworks to ensure their safe, equitable, and accountable use.