标题： 临床心理健康人工智能系统的数据集综述 显示英文标题

标题： A Comprehensive Review of Datasets for Clinical Mental Health AI Systems

摘要： 心理健康障碍在全球范围内正在上升。 然而，受过训练的临床医生的可用性并未按比例增长，导致许多人无法获得充分或及时的支持。 为了弥补这一差距，最近的研究表明人工智能（AI）在辅助心理健康诊断、监测和干预方面具有潜力。 然而，开发高效、可靠和道德的AI来协助临床医生，高度依赖于高质量的临床训练数据集。 尽管人们对用于训练临床AI助手的数据整理兴趣日益增长，但现有的数据集大多仍然分散、记录不足且常常无法访问，阻碍了为临床心理健康护理开发的AI模型的可重复性、可比性和泛化能力。 在本文中，我们介绍了针对训练和开发人工智能驱动的临床助手的临床心理健康数据集的首次全面调查。 我们将这些数据集按心理健康障碍（例如，抑郁、精神分裂症）、数据模态（例如，文本、语音、生理信号）、任务类型（例如，诊断预测、症状严重程度估计、干预生成）、可访问性（公开、受限或私有）以及社会文化背景（例如，语言和文化背景）进行分类。 除了这些，我们还研究了合成的临床心理健康数据集。 我们的调查指出了关键的空白，例如缺乏纵向数据、有限的文化和语言代表性、收集和注释标准不一致，以及合成数据中模态的缺乏。 最后，我们概述了在整理和标准化未来数据集中的关键挑战，并提供了可行的建议，以促进更强大、泛化能力和公平的心理健康AI系统的发展。 显示英文摘要

摘要： Mental health disorders are rising worldwide. However, the availability of trained clinicians has not scaled proportionally, leaving many people without adequate or timely support. To bridge this gap, recent studies have shown the promise of Artificial Intelligence (AI) to assist mental health diagnosis, monitoring, and intervention. However, the development of efficient, reliable, and ethical AI to assist clinicians is heavily dependent on high-quality clinical training datasets. Despite growing interest in data curation for training clinical AI assistants, existing datasets largely remain scattered, under-documented, and often inaccessible, hindering the reproducibility, comparability, and generalizability of AI models developed for clinical mental health care. In this paper, we present the first comprehensive survey of clinical mental health datasets relevant to the training and development of AI-powered clinical assistants. We categorize these datasets by mental disorders (e.g., depression, schizophrenia), data modalities (e.g., text, speech, physiological signals), task types (e.g., diagnosis prediction, symptom severity estimation, intervention generation), accessibility (public, restricted or private), and sociocultural context (e.g., language and cultural background). Along with these, we also investigate synthetic clinical mental health datasets. Our survey identifies critical gaps such as a lack of longitudinal data, limited cultural and linguistic representation, inconsistent collection and annotation standards, and a lack of modalities in synthetic data. We conclude by outlining key challenges in curating and standardizing future datasets and provide actionable recommendations to facilitate the development of more robust, generalizable, and equitable mental health AI systems.