大模型助心理诊疗：从情绪识别到个性化支持，兼顾伦理安全
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
- 整合社交媒体、病历等多源数据，用大模型实现抑郁早筛与风险评估
- 通过提示工程与多模态融合，提升诊断准确性与治疗个性化水平
- 聚焦伦理挑战，呼吁建立可信赖的临床部署框架
我们系统回顾了大型语言模型（LLMs）在心理健康领域的应用，涵盖社交媒体分析、临床对话代理、治疗辅助工具、提示工程、多模态学习及伦理考量。通过整合社交媒体帖子、电子病历和多模态输入等多样化数据源，推动抑郁症早期检测、自杀风险评估、个性化治疗支持及心理教育内容生成。本综述揭示了大模型与标注策略的进展，提升了可解释性与临床相关性，强调提示工程对领域适配的关键作用。同时探讨了融合文本、语音与传感器数据的新兴多模态融合技术，以优化心理健康诊断与监测。最后，我们指出持续存在的伦理、社会技术与监管挑战，倡导建立安全、公平且可问责的部署框架，确保大模型在真实心理医疗场景中的可靠应用。
原文摘要 · Abstract (English)
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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