MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE APPLICATIONS IN OBSESSIVE-COMPULSIVE DISORDER: A COMPREHENSIVE REVIEW

Abstract

Obsessive-Compulsive Disorder (OCD) is characterized by persistent and often debilitating intrusive thoughts and compulsive behaviors. Response rates vary significantly across people, and the trial-and-error approach in prescribing remains a substantial therapeutic challenge, despite the prevalent use of pharmaceutical treatments, particularly SSRIs. Advancements in machine learning (ML) have shown innovative opportunities for improving and personalizing OCD therapy. This research examines pharmacological repurposing, the characterization of adverse effects, and predictive modeling of treatment responses related to OCD medications. Supervised learning systems have shown potential in identifying biomarkers and clinical variables that predict individual responses to SSRIs and adjunctive drugs. Unsupervised learning techniques have facilitated the identification of OCD subgroups that may have varying responses to pharmacological treatments. Data heterogeneity, limited sample sizes, and the need for interpretability persist as obstacles, notwithstanding recent advancements. Machine learning in clinical decision-making may improve precision psychiatry for obsessive-compulsive disorder (OCD), perhaps resulting in safer, more effective, and expedited treatment methods.