综述AI在抑郁检测中的应用，梳理主流方法与数据趋势
AI Models for Depressive Disorder Detection and Diagnosis: A Review
- 按诊断/预测、模态、模型分类构建新体系
- 图神经网络主导脑连接建模，大语言模型兴起于语言分析
- 关注多模态融合与算法公平性，适合研究者参考
重度抑郁症是全球主要致残原因，但诊断仍依赖主观临床评估。融合人工智能有望发展客观、可扩展、及时的诊断工具。本文基于对55项关键研究的系统综述，全面回顾当前最先进的抑郁检测与诊断AI方法。提出一种新型分层分类体系，按主要临床任务（诊断与预测）、数据模态（文本、语音、神经影像、多模态）和计算模型类别（如图神经网络、大语言模型、混合方法）进行结构化归纳。深入分析揭示三大趋势：图神经网络在脑连接建模中占主导；大语言模型在语言与对话数据中快速兴起；多模态融合、可解释性与算法公平性成为新兴焦点。除方法洞察外，还梳理了主要公开数据集与标准评估指标，为研究者提供实践指南。通过整合现有进展并指出开放挑战，本综述为计算精神病学未来发展提供全景路线图。
原文摘要 · Abstract (English)
Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical assessments. Integrating Artificial Intelligence (AI) holds promise for developing objective, scalable, and timely diagnostic tools. In this paper, we present a comprehensive survey of state-of-the-art AI methods for depression detection and diagnosis, based on a systematic review of 55 key studies. We introduce a novel hierarchical taxonomy that structures the field by primary clinical task (diagnosis vs. prediction), data modality (text, speech, neuroimaging, multimodal), and computational model class (e.g., graph neural networks, large language models, hybrid approaches). Our in-depth analysis reveals three major trends: the predominance of graph neural networks for modeling brain connectivity, the rise of large language models for linguistic and conversational data, and an emerging focus on multimodal fusion, explainability, and algorithmic fairness. Alongside methodological insights, we provide an overview of prominent public datasets and standard evaluation metrics as a practical guide for researchers. By synthesizing current advances and highlighting open challenges, this survey offers a comprehensive roadmap for future innovation in computational psychiatry.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。