Large Language Models in Mental Health: A Systematic Review of Applications, Innovations and Ethical Challenges
Year: 2025
Type: review
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.
Authors: Yisong Chen, Yifan Gao, Sijing Yu, Chuqing Zhao, Yang Lu
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7117753069