梳理AI在心理健康的五大应用，助力精准筛查与干预
A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education
- 构建四支柱框架，整合筛查、治疗、监测等五阶段应用
- 36项研究显示AI可缩短等待时间并提升患者参与度
- 适合临床医生、研究人员及政策制定者参考落地
人工智能驱动的数字干预技术（包括生成式AI和以人为中心的AI）正广泛应用于心理健康领域，以拓展数字精神卫生服务的可及性。本篇遵循PRISMA-ScR指南的范围综述，系统梳理了截至2024年初的36项实证研究，覆盖心理健康照护的五个关键环节：治疗前（筛查/分诊）、治疗中（支持）、治疗后（远程监测）、临床教育以及人群级预防。重点聚焦大语言模型（LLMs）、机器学习（ML）模型及自主对话代理的应用，典型场景包括转诊分诊、共情沟通增强及聊天机器人与语音助手提供的辅助心理治疗。尽管可降低等待时间、提升患者参与度，仍面临算法偏见、数据隐私与人机协作障碍等挑战。本文提出一个新颖的四支柱框架，为未来安全、有效且公平的AI辅助心理健康干预提供全面路线图，为研究者、临床医师与政策制定者提供可操作洞见。
原文摘要 · Abstract (English)
Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.
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