Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
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.
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[Submitted on 31 May 2026]
Title:Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
View a PDF of the paper titled Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges, by Yisong Chen and 3 other authors
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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.
Comments: Systematic review. Published in Journal of Industrial Integration and Management (2025). Applications of large language models in mental health, including social media analysis, clinical conversational agents, therapy support tools, multimodal learning, and ethical considerations
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.18080 [cs.AI]
(or arXiv:2608.18080v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.18080
arXiv-issued DOI via DataCite
Journal reference: Journal of Industrial Integration and Management (JIIM), 2025
Related DOI:
https://doi.org/10.1142/S2424862225300042
DOI(s) linking to related resources
Submission history
From: Yisong Chen [view email] [v1] Sun, 31 May 2026 01:55:20 UTC (1,199 KB)
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