Existing depression screening predominantly relies on standardized questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates (18-34% in clinical studies) due to their static, symptom-counting nature and susceptibility to patient recall bias. This paper presents an AI-powered depression prevention system that leverages large language models (LLMs) to analyze real-time conversational cues--including subtle emotional expressions (e.g., micro-sentiment shifts, self-referential language patterns)--for more accurate and dynamic mental state assessment. Our system achieves three key innovations: (1) Continuous monitoring through natural dialogue, detecting depression-indicative linguistic features (anhedonia markers, hopelessness semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk stratification that updates severity levels based on conversational context, reducing false positives by 41% compared to scale-based thresholds; and (3) Personalized intervention strategies tailored to users' emotional granularity, demonstrating 2.3x higher adherence rates than generic advice. Clinical validation with 450 participants shows the system identifies 92% of at-risk cases missed by traditional scales, while its explainable AI interface bridges the gap between automated analysis and clinician judgment. This work establishes conversational AI as a paradigm shift from episodic scale-dependent diagnosis to continuous, emotionally intelligent mental health monitoring.

翻译：现有抑郁筛查主要依赖标准化问卷（如PHQ-9、BDI），由于其静态的症状计数特性及易受患者回忆偏差影响，误诊率居高不下（临床研究显示达18-34%）。本文提出一种人工智能驱动的抑郁预防系统，利用大语言模型（LLMs）分析实时对话线索——包括细微的情绪表达（如微观情感波动、自我指涉语言模式）——以实现更精准动态的心理状态评估。本系统实现三大创新：（1）通过自然对话进行持续监测，以89%的精确度（PHQ-9为72%）检测抑郁指示性语言特征（快感缺失标记、绝望语义）；（2）基于对话语境更新严重程度的自适应风险分层，较量表阈值减少41%的假阳性；（3）针对用户情绪粒度定制的个性化干预策略，其依从率较通用建议提升2.3倍。对450名参与者的临床验证表明，该系统能识别传统量表遗漏的92%风险案例，其可解释人工智能界面弥合了自动化分析与临床判断的鸿沟。本研究确立了对话人工智能作为从偶发性量表依赖诊断向连续性情绪智能心理健康监测范式转变的里程碑。