综述大模型在心理治疗中的应用现状与挑战
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
- 按评估、诊断、治疗三阶段构建心理治疗大模型分类框架
- 现有研究集中于常见障碍，存在语言与文化偏差
- 适合关注心理AI、临床辅助系统的研究人员参考
心理健康在当代医疗中日益重要，心理治疗需要动态、情境敏感的交互，传统NLP方法难以满足。大语言模型（LLMs）因其具备长上下文处理与多轮推理能力，有望弥补这一差距。本文提出一个概念性分类框架，将心理治疗划分为评估、诊断与治疗三个相互关联阶段，系统分析LLM在该领域的进展与挑战。综合分析揭示当前研究存在不平衡：过度聚焦常见障碍、语言偏见、方法碎片化及理论整合不足。关键挑战包括捕捉症状动态变化、克服语言与文化偏见、保障诊断可靠性。未来方向应推动连续多阶段建模、基于心理学理论的实时自适应系统，以及覆盖更广泛精神障碍与疗法的多样化研究，以实现更全面、临床融合的心理治疗大模型系统。
原文摘要 · Abstract (English)
Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages--assessment, diagnosis, and treatment--to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.
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