Artificial intelligence for the diagnosis and treatment response prediction of obsessive-compulsive disorder: A narrative review

Abstract

Obsessive-compulsive disorder (OCD) exists as a persistent psychiatric condition which produces different symptoms that lead to severe personal distress and complete social and work disability and major expenses for communities. The field of neurobiology has made progress but doctors still face two main clinical obstacles which include delayed diagnoses and inconsistent treatment outcomes and numerous cases of medication failure. The field of psychiatry now benefits from two separate developments which include artificial intelligence (AI) advancements and psychiatric treatment method improvements. This study is a narrative review that synthesizes existing evidence on the application of AI in OCD, with a focus on diagnostic models, treatment response prediction, and clinical decision support systems. The research combines existing data about AI usage in OCD treatment through its evaluation of diagnostic methods and treatment prediction models and clinical decision systems. The research team performed a thorough evaluation of studies which used machine learning and deep learning models to analyze neuroimaging data and electrophysiological signals and clinical scales and digital phenotyping information and multiple data sources. The research evaluated two new roles of large language models together with explainable AI methods which assess their potential for medical interpretation and their ethical implications and their ability to translate into practice. The research findings show that AI-based models demonstrate successful results in identifying OCD patients from healthy participants and in forecasting treatment outcomes for pharmacological and psychotherapeutic and neuromodulation interventions. Research shows that models which use multiple data sources produce better results than systems which depend on a single data source because OCD exists as a complex combination of neurological and behavioral elements. The reported study results show significant differences between research investigations because the studies used different data sources and testing methods and validation approaches. The current limitations in explainability together with insufficient external validation and small available data samples prevent these models from being used in clinical practice. AI provides substantial potential to enhance precision psychiatric care for OCD patients through its ability to detect conditions earlier and create personalized treatment plans and provide continuous clinical guidance. The process of translating this technology into medical practice needs research studies which must be conducted in multiple centers throughout different time periods while using AI systems that provide explanations and follow human-friendly design principles. The deployment of AI tools for obsessive-compulsive disorder management requires solutions to address ethical issues and regulatory requirements and interpretability problems for safe and dependable and enduring implementation.

Keywords: Obsessive-compulsive disorder; Artificial intelligence; Machine learning; Deep learning; Clinical decision support systems; Precision psychiatry; Neuroimaging; Treatment outcome; Explainable artificial intelligence

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.