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Articles Published Processes
6/30/2026 11:45:11 AM | Browse: 1 | Download: 11
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Received |
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2025-12-08 06:15 |
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Peer-Review Started |
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2025-12-08 06:15 |
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First Decision by Editorial Office Director |
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2026-01-14 03:12 |
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Return for Revision |
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2026-01-14 03:12 |
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Revised |
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2026-01-25 00:09 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-03-09 02:42 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-03-09 06:15 |
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Articles in Press |
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2026-03-09 06:15 |
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Edit the Manuscript by Language Editor |
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2026-03-18 17:45 |
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Typeset the Manuscript |
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2026-06-15 00:13 |
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Publish the Manuscript Online |
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2026-06-30 11:45 |
| ISSN |
2220-3206 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Psychiatry |
| Manuscript Type |
Review |
| Article Title |
Artificial intelligence and major depression: Toward mechanistic and clinically actionable models
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Filiz Ozsoy, Gulay Tasci, Burak Tasci, Sengul Dogan and Turker Tuncer |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Burak Tasci, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr |
| Key Words |
Major depressive disorder; Artificial intelligence; Computational psychiatry; Machine learning; Multimodal data integration; Precision psychiatry |
| Core Tip |
This review synthesizes contemporary evidence showing that artificial intelligence is reshaping the scientific and clinical understanding of major depressive disorder (MDD) by connecting epidemiological patterns, etiological mechanisms, and neurobiological findings with advanced computational models. Unlike traditional symptom-based diagnostic systems, AI-driven approaches integrate multimodal data - including neuroimaging, 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. |
| Publish Date |
2026-06-30 11:45 |
| Citation |
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
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| URL |
https://www.wjgnet.com/2220-3206/full/v16/i7/117452.htm |
| DOI |
https://doi.org/10.5498/wjp.117452 |
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