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: 203 Days and 0.8 Hours
Core Tip
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.
