心理健康领域的大语言模型：应用、创新与伦理挑战的系统综述
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
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中文总结 AI 辅助
该系统综述梳理了大语言模型在心理健康领域的多类应用、技术创新，同时探讨了其面临的伦理与监管挑战，并倡导建立保障其安全公平部署的框架。
中文摘要 AI 辅助
本文对大语言模型（LLMs）在健康领域的应用展开综述，涵盖社交媒体分析、临床对话智能体、治疗支持工具、提示工程、多模态学习及伦理考量等方面。我们整合了跨学科研究的成果，这些研究利用社交媒体帖子、电子病历、多模态输入等各类数据源，实现抑郁早期检测、自杀风险评估、个性化治疗支持及心理教育内容生成。本综述强调了LLMs模型与标注策略在提升可解释性和临床相关性方面的进展，同时凸显了提示工程对领域适配的关键作用。我们还探讨了整合文本、语音与传感器数据以优化心理健康诊断和监测的新兴多模态融合技术。最后，本文讨论了持续存在的伦理、社会技术及监管挑战，并倡导建立框架以确保LLMs在现实心理健康护理中的安全、公平且负责任的部署。
英文摘要
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.
