# A Comprehensive Review of Datasets for Clinical Mental Health AI Systems arXiv: https://arxiv.org/abs/2508.09809 ยท HTML: https://mlsanity.com/paper/2508.09809/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 171 references that are in the map. - First posted on arXiv 13 Aug 2025. ## Builds on (most cited first, 30 of 30) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023) - [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023) - [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021) - [CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling](https://mlsanity.com/paper/2405.16433/) (2024) - [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019) - [Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory](https://mlsanity.com/paper/2407.03103/) (2024) - [HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy](https://mlsanity.com/paper/2403.05574/) (2024) - [SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://mlsanity.com/paper/2305.00450/) (2023) - [CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering](https://mlsanity.com/paper/2403.16008/) (2024) - [ChatCounselor: A Large Language Models for Mental Health Support](https://mlsanity.com/paper/2309.15461/) (2023) - [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025) - [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020) - [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022) - [D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat](https://mlsanity.com/paper/2205.11764/) (2022) - [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023) - [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://mlsanity.com/paper/2106.13973/) (2021) - [MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents](https://mlsanity.com/paper/2408.12142/) (2024) - [MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance](https://mlsanity.com/paper/2504.13211/) (2025) - [MEDIC: A Multimodal Empathy Dataset in Counseling](https://mlsanity.com/paper/2305.02842/) (2023) - [Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations](https://mlsanity.com/paper/2504.13955/) (2025) - [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024) - [Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges](https://mlsanity.com/paper/2407.16804/) (2024) - [Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning](https://mlsanity.com/paper/2502.06873/) (2025) - [Predictive Linguistic Features of Schizophrenia](https://mlsanity.com/paper/1810.09377/) (2018) - [Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress](https://mlsanity.com/paper/2007.15815/) (2020) - [A Multimodal Framework for the Assessment of the Schizophrenia Spectrum](https://mlsanity.com/paper/2406.09706/) (2024) - [A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context](https://mlsanity.com/paper/2501.06859/) (2025) - [Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2502.00451/) (2025) - [Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity](https://mlsanity.com/paper/1512.09133/) (2015) ## Built on by (most cited first, 3 of 3) - [Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs](https://mlsanity.com/paper/2604.20382/) (2026) - [coTherapist: A Behavior-Aligned Small Language Model to Support Mental Healthcare Experts](https://mlsanity.com/paper/2601.10246/) (2026) - [When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling](https://mlsanity.com/paper/2606.04389/) (2026) ## Abstract (arXiv) 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.