Ozsoy F, Tasci G, Tasci B, Dogan S, Tuncer T. Artificial intelligence for the diagnosis and treatment response prediction of obsessive-compulsive disorder: A narrative review. World J Psychiatry 2026; 16(7): 118161 [DOI: 10.5498/wjp.118161]
Corresponding Author of This Article
Burak Tasci, PhD, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
review-article
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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/
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Ozsoy F, Tasci G, Tasci B, Dogan S, Tuncer T. Artificial intelligence for the diagnosis and treatment response prediction of obsessive-compulsive disorder: A narrative review. World J Psychiatry 2026; 16(7): 118161 [DOI: 10.5498/wjp.118161]
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 review and editing; Tasci G and Tasci B contributed to clinical literature review, data interpretation, writing review, conceptualization, methodology, artificial intelligence-related content development, writing, original draft, supervision as co-corresponding authors; 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; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Burak Tasci, PhD, Vocational School of Technical Sciences, Firat University, Cahit Arf Street, Elazig 23119, Türkiye. btasci@firat.edu.tr
Received: December 25, 2025 Revised: January 25, 2026 Accepted: March 2, 2026 Published online: July 19, 2026 Processing time: 183 Days and 5 Hours
Core Tip
Core Tip: This review highlights that artificial intelligence (AI) enables earlier and more accurate obsessive-compulsive disorder identification by integrating multimodal biomarkers (magnetic resonance imaging, electroencephalography, clinical scales, and digital phenotyping), surpassing unimodal models. Its key innovation is the joint clinical role of explainable AI for transparent neurobiological interpretation and large language models for personalized, scalable clinical decision support. Limited explainability, small samples, and weak external validation remain the main barriers to clinical translation. Future obsessive-compulsive disorder care requires multicenter, longitudinal, and clinician-aligned explainable AI systems to ensure ethical, regulatory, and trustworthy implementation.
