待翻譯：Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：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.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [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 View PDF 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) Full-text links: Access Paper: 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 View PDF view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)