Obsessive-compulsive disorder (OCD) is a debilitating mental health condition that affects millions worldwide. Despite advancements in pharmacological and psychotherapeutic treatments, a significant subset of patients remains resistant to conventional interventions. In recent years, artificial intelligence (AI) has emerged as a promising tool in mental health, offering innovative solutions for diagnosis, treatment personalization, and research in OCD. This narrative review examines the applications and efficacy of AI-driven approaches in treating OCD. Machine learning algorithms facilitate early diagnosis, predict treatment outcomes, and optimize pharmacological interventions by analyzing neuroimaging and clinical data. AI-enhanced neurostimulation, such as closed-loop deep brain stimulation, has shown promise for treating cases resistant to conventional therapies. At the same time, natural language processing enhances diagnostic accuracy by extracting patterns from patient histories. Additionally, AI-powered neurofeedback and virtual therapy platforms enhance exposure and response prevention therapy, increasing treatment accessibility and effectiveness. However, data privacy, algorithmic transparency, and ethical considerations remain. This review highlights the transformative potential of AI in OCD treatment while also addressing its limitations and future directions in this rapidly evolving field.
Seyed Javad Masoumi (Sun,) studied this question.
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