AI Models for Depressive Disorder Detection and Diagnosis: A Review

2025-08-16

AI Summary

Current clinical diagnosis of Major Depressive Disorder (MDD) relies heavily on subjective assessments, lacking objective, scalable, and standardized tools. Method: We systematically review 55 AI-driven studies on MDD detection and diagnosis, proposing the first hierarchical taxonomy for computational psychiatry—structured along three dimensions: clinical tasks, data modalities, and model architectures. Contribution/Results: We identify three emerging technical trends: graph neural networks, large language models, and multimodal fusion (integrating neuroimaging, speech, and text). We further emphasize interpretability and algorithmic fairness as critical requirements. Additionally, we catalog widely used public datasets and evaluation metrics, offering practical implementation guidance. Collectively, this work delivers a scientifically grounded and technically feasible roadmap for AI-assisted MDD diagnosis, bridging translational gaps between computational methods and clinical psychiatry.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Developing objective AI tools for depression diagnosis
• Reviewing AI methods across diverse data modalities
• Addressing challenges in explainability and algorithmic fairness

Innovation

• Hierarchical taxonomy structures clinical tasks and data
• Graph neural networks model brain connectivity effectively
• Large language models analyze linguistic data for diagnosis