Ozsoy F, Tasci G, Tasci B, Dogan S, Tuncer T. Artificial intelligence and major depression: Toward mechanistic and clinically actionable models. World J Psychiatry 2026; 16(7): 117452 [DOI: 10.5498/wjp.117452]
Corresponding Author of This Article
Burak Tasci, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Research Domain of This Article
Psychiatry
Article-Type of This Article
review-article
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Filiz Ozsoy, Department of Psychiatry, Tokat Gaziosmanpasa University, Tokat 60100, Türkiye
Gulay Tasci, Department of Psychiatry, Elazig Fethi Sekin City Hospital, Elazig 23100, Türkiye
Burak Tasci, Vocational School of Technical Sciences, Firat University, Elazig 23119, Türkiye
Sengul Dogan, Turker Tuncer, Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Türkiye
Co-corresponding authors: Gulay Tasci and Burak Tasci.
Author contributions: Ozsoy F contributed to conceptualization, clinical content supervision, validation, writing and editing of the manuscript; Tasci G contributed to clinical literature review, data interpretation, and writing of the manuscript; Tasci B contributed to conceptualization, methodology, AI-related content development, writing, original draft, supervision; Tasci G and Tasci B are designated as the co-corresponding authors. Dogan S contributed to AI methodology analysis, data curation, writing, technical sections; Tuncer T contributed to computational modeling review, technical validation, writing, review and editing.
AI contribution statement: No part of the main text of the manuscript, including the Abstract, Introduction, Materials and Methods, Results, Discussion, or Conclusion, was AI-generated. AI-assisted tools (Grammarly and DeepL) were used only for language polishing, grammar checking, and translation support. They were not used for data analysis or scientific writing assistance. No images or figures in the manuscript were generated by AI. All figures were prepared by the authors.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Corresponding author: Burak Tasci, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Received: December 8, 2025 Revised: January 25, 2026 Accepted: March 9, 2026 Published online: July 19, 2026 Processing time: 202 Days and 23.6 Hours
Abstract
Major depressive disorder (MDD) is a widespread psychiatric disorder driven by complex genetic, neurobiological, psychological, and environmental mechanisms. Conventional diagnostic systems, such as the Diagnostic and Statistical Manual of Mental Disorders, the Fifth Edition and International Classification of Diseases, rely on symptom-based evaluations, which are limited by subjectivity, symptom overlap, and restricted applicability across diverse populations. Advances in artificial intelligence (AI) provide new opportunities for objective, data-driven depression assessment. This review synthesizes epidemiological, etiological, and clinical evidence to evaluate AI-based approaches for depression detection and characterization. Machine learning, deep learning, and large language model-based methods applied to multimodal data, including electronic health records, neuroimaging, electroencephalography (EEG), speech and language data, and digital behavioral signals, were systematically examined, with particular attention to interpretability and ethical considerations. Depression was consistently associated with monoaminergic and neurotrophic dysregulation, inflammation, hypothalamic pituitary adrenal axis dysfunction, and frontolimbic network abnormalities. AI models demonstrated strong discriminative performance using biological and behavioral markers, particularly when multimodal data integration was employed. Neuroimaging and EEG analyses revealed network-level alterations, while natural language processing approaches captured linguistic and acoustic markers linked to symptom severity and suicide risk. AI-based systems have substantial potential to advance precision psychiatry by enabling earlier detection and personalized treatment of depression. However, challenges, including dataset bias, methodological heterogeneity, limited interpretability, and insufficient real-world validation, must be addressed through standardized, transparent, and ethically guided clinical research.
Core Tip: This review synthesizes contemporary evidence showing that artificial intelligence is reshaping the scientific and clinical understanding of major depressive disorder by connecting epidemiological patterns, etiological mechanisms, and neurobiological findings with advanced computational models. Unlike traditional symptom-based diagnostic systems, artificial intelligence (AI)-driven approaches integrate multimodal data - including neuroimaging, electroencephalography (EEG), speech, language, behavioral traces, and clinical records - to generate mechanistic insights, stratify patient risk, and support individualized treatment planning. The review highlights how graph-based neuroimaging models, deep learning analysis of EEG time - frequency signatures, and large language models for clinical narrative interpretation collectively form a new computational framework for precision psychiatry. It also underscores the key challenges - such as data heterogeneity, cultural bias, privacy risks, and limited real-world validation - that must be addressed to translate AI systems into trustworthy and clinically actionable tools.
