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AI Models for Depressive Disorder Detection and Diagnosis: A Review

arXiv 2508.12022 ↗First posted 16 Aug 2025cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 89 references that are in the map. First posted on arXiv 16 Aug 2025.

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27 of 89 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  5. 2023A Survey of Large Language Modelscited by 2,576
  6. 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 156
  7. 2020MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysiscited by 38
  8. 2022Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnairescited by 27
  9. 2025Learning Efficient Positional Encodings with Graph Neural Networkscited by 24
  10. 2022BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneckcited by 17
  11. 2024When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detectioncited by 17
  12. 2023Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Mediacited by 16
15 more
  1. 2021Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identificationcited by 15
  2. 2025U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detectioncited by 12
  3. 2025Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Modelscited by 8
  4. 2023Content Augmented Graph Neural Networkscited by 7
  5. 2025Machine Learning Fairness for Depression Detection using EEG Datacited by 7
  6. 2025SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Informationcited by 7
  7. 2023Dynamic Graph Representation Learning for Depression Screening with Transformercited by 4
  8. 2024Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Modelscited by 4
  9. 2024Strong Transitivity Relations and Graph Neural Networkscited by 3
  10. 2025DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessmentcited by 3
  11. 2025Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generationcited by 3
  12. 2024MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detectioncited by 2
  13. 2024Multi Class Depression Detection Through Tweets using Artificial Intelligencecited by 2
  14. 2024A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detectioncited by 2
  15. 2025Frequency Feature Fusion Graph Network For Depression Diagnosis Via fNIRScited by 1

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