Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
Summary
This systematic review examines the applications of large language models in mental health, covering innovations in areas like clinical conversational agents and multimodal learning, while highlighting ethical challenges and advocating for safe deployment frameworks.
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# Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges Source: [https://arxiv.org/abs/2608.18080](https://arxiv.org/abs/2608.18080) [View PDF](https://arxiv.org/pdf/2608.18080) > 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\. ## Submission history From: Yisong Chen \[[view email](https://arxiv.org/show-email/ced3fb6c/2608.18080)\] **\[v1\]**Sun, 31 May 2026 01:55:20 UTC \(1,199 KB\)
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