Artificial intelligence and major depression: Toward mechanistic and clinically actionable models

doi:10.5498/wjp.117452

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Jul 19, 2026; 16(7): 117452
Published online Jul 19, 2026. doi: 10.5498/wjp.117452

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Figure 3 Comprehensive artificial intelligence framework for depression detection: From data sources to clinical application. EHRs: Electronic health records; fMRI: Functional magnetic resonance imaging; sMRI: Structural magnetic resonance imaging; DTI: Diffusion tensor imaging; EEG: Electroencephalography; TMS: Transcranial magnetic stimulation; AI: Artificial intelligence; SHAP: SHapley Additive exPlanation; MDD: Major depressive disorder; PHQ-9: Patient Health Questionnaire 9; TRD: Treatment-resistant depression.

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
URL: https://www.wjgnet.com/2220-3206/full/v16/i7/117452.htm
DOI: https://dx.doi.org/10.5498/wjp.117452

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]

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