# AI Models for Depressive Disorder Detection and Diagnosis: A Review

arXiv: https://arxiv.org/abs/2508.12022 · HTML: https://mlsanity.com/paper/2508.12022/

## 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.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires](https://mlsanity.com/paper/2204.10432/) (2022)
- [Learning Efficient Positional Encodings with Graph Neural Networks](https://mlsanity.com/paper/2502.01122/) (2025)
- [BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck](https://mlsanity.com/paper/2205.03612/) (2022)
- [When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection](https://mlsanity.com/paper/2402.13276/) (2024)
- [Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media](https://mlsanity.com/paper/2305.05138/) (2023)
- [Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification](https://mlsanity.com/paper/2107.12838/) (2021)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models](https://mlsanity.com/paper/2502.05879/) (2025)
- [Content Augmented Graph Neural Networks](https://mlsanity.com/paper/2311.12741/) (2023)
- [Machine Learning Fairness for Depression Detection using EEG Data](https://mlsanity.com/paper/2501.18192/) (2025)
- [SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information](https://mlsanity.com/paper/2502.10950/) (2025)
- [Dynamic Graph Representation Learning for Depression Screening with Transformer](https://mlsanity.com/paper/2305.06447/) (2023)
- [Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models](https://mlsanity.com/paper/2409.14794/) (2024)
- [Strong Transitivity Relations and Graph Neural Networks](https://mlsanity.com/paper/2401.01384/) (2024)
- [DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment](https://mlsanity.com/paper/2501.14985/) (2025)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)
- [MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detection](https://mlsanity.com/paper/2403.15485/) (2024)
- [Multi Class Depression Detection Through Tweets using Artificial Intelligence](https://mlsanity.com/paper/2404.13104/) (2024)
- [A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection](https://mlsanity.com/paper/2410.18103/) (2024)
- [Frequency Feature Fusion Graph Network For Depression Diagnosis Via fNIRS](https://mlsanity.com/paper/2504.21064/) (2025)

## 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.