REWRITING THE ADDICTION NARRATIVE THROUGH ARTIFICIAL INTELLIGENCE IN THE PREDICTION, SCREENING, AND TREATMENT OF SUBSTANCE USE DISORDERS AND BEHAVIOURAL ADDICTION

Background: Substance Use Disorders (SUD\'s) and Behavioural Addiction are intricately related constructs that shed light on the coherent conceptual paradigm of addiction science which involve a shared pathological disruption of neural mechanisms. SUD is a medical, psychiatric condition defined as a pattern of substance use leading to clinically significant impairment, characterized by compulsive consumption, and continued use despite harmful consequences. Behavioural addiction involves a compulsion to engage in a rewarding non drug-related behavior despite any negative consequences to the person\'s physical, mental, social, financial well-being. The use of artificial intelligence in the treatment, understanding of SUD and behavioral addictions is a pivotal advancement in the new era of data-driven therapeutic innovation. Objective: This review synthesizes existing empirical studies examining the use of Al-based tools such as machine learning models, mobile health applications, and digital therapeutics in SUD care along with the advancements in clinical theories offering a foundation for more coherent development, evaluation along with possible uses and the challenges implementation of Al-supported interventions for SUDs and behavioural addiction. Methods: A systematic search of major databases, including ScienceDirect, SpringerNature, ResearchGate, Lancet, National Library of Medicine and Elsevier were conducted to identify relevant studies. Data extraction, quality assessment was performed using standardized tools. Result: Findings indicate that Al has been used primarily for relapse prediction, treatment personalization, patient monitoring with promising but heterogeneous results. Ethical challenges, methodological limitations were discussed. The review highlights the need for standardized evaluation methods and theory-informed development of Al interventions for SUDs. Keywords: Artificial Intelligence (Al), Machine Learning (ML), Substance Use Disorders (SUD)