Ozsoy F, Tasci G, Tasci B, Dogan S, Tuncer T. Schizophrenia in the age of artificial intelligence: A review of advances in diagnosis, prediction, and digital psychiatry. World J Psychiatry 2026; 16(5): 116452 [DOI: 10.5498/wjp.v16.i5.116452]
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
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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/
May 19, 2026 (publication date) through May 5, 2026
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Journal Information of This Article
Publication Name
World Journal of Psychiatry
ISSN
2220-3206
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Ozsoy F, Tasci G, Tasci B, Dogan S, Tuncer T. Schizophrenia in the age of artificial intelligence: A review of advances in diagnosis, prediction, and digital psychiatry. World J Psychiatry 2026; 16(5): 116452 [DOI: 10.5498/wjp.v16.i5.116452]
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
Author contributions: Ozsoy F contributed to conceptualization, clinical content supervision, validation, writing review and editing; Tasci G contributed to clinical literature review, data interpretation, writing review; Tasci B contributed to conceptualization, methodology, artificial intelligence-related content development, writing, original draft, supervision; Dogan S contributed to artificial intelligence methodology analysis, data curation, writing, technical sections; Tuncer T contributed to computational modeling review, technical validation, writing, review and editing.
Conflict-of-interest statement: The authors declare that there are no conflicts of interest related to this work. No financial, personal, or professional relationships have influenced the preparation or submission of this manuscript.
Corresponding author: Burak Tasci, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Received: November 12, 2025 Revised: December 6, 2025 Accepted: February 3, 2026 Published online: May 19, 2026 Processing time: 169 Days and 0.4 Hours
Core Tip
Core Tip: Schizophrenia remains one of the most complex psychiatric disorders, characterized by profound neurobiological, cognitive, and social dysfunctions. Despite decades of clinical and neuroimaging research, diagnosis still depends largely on subjective observation. This review integrates classical perspectives on epidemiology, etiology, and treatment with recent developments in artificial intelligence (AI). It highlights how AI-driven models-particularly those analyzing neuroimaging and electroencephalography data-offer objective insights into brain alterations and symptom mechanisms. By bridging traditional psychiatry with emerging computational tools, the paper outlines a roadmap for early detection, personalized intervention, and transparent decision support in schizophrenia care.
