一款专为心理健康设计的生成式AI聊天机器人，在真实世界中展现持续改善效果。
Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot
- 基于大模型构建心理健康专用AI，支持个性化对话与安全干预。
- 6周内抑郁焦虑症状显著下降，10周后仍保持改善，社交连接感提升明显。
- 高参与度与良好医患关系，适合需可及性支持的大众人群使用。
专为心理健康设计的生成式人工智能（GAI）聊天机器人可提供安全、个性化且可扩展的心理健康支持。本研究评估了一个面向心理健康的基础模型，在2025年5月15日至9月15日期间，成年用户在使用聊天机器人期间完成心理测评。参与者自愿同意，填写人口统计信息、心理健康症状、社会联结感及自我设定目标。每两周重复测量一次，共持续6周，10周时进行最终随访。分析包括效应量和增长混合模型，识别不同参与者群体及其特征。结果显示，PHQ-9和GAD-7评分显著降低，并在随访时持续改善。希望感、行为激活、社交互动、孤独感及感知社会支持均有显著提升，且在10周随访中维持稳定。用户参与度高，且与结果正相关。治疗联盟水平相当于传统治疗，也预测了积极结果。自动化安全防护机制按设计运行，共76次会话被标记为风险，均已依政策处理。该单臂自然观察研究初步证明，心理健康专用的GAI基础模型可在真实环境中提供可及、有吸引力、有效且安全的心理支持。这些发现支持早期随机研究的结果，为未来在真实场景中开展心理健康GAI研究提供了前景。
原文摘要 · Abstract (English)
Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings.
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