A Systematic Review of Predictive Modeling and Personalized Interventions in the Clinical Management of Opioid Use Disorder

Abstract

Opioid Use Disorder (OUD) is characterized by chronic relapse, high mortality rates, and significant socioeconomic burdens. Conventional clinical mehtods often struggle with diagnostic delays and the inability to provide real-time, personalized monitoring. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into addiction medicine offers a transformative pathway for optimizing patient outcomes. Through the deployment of sophisticated algorithms ranging from logistic regression and random forests to gradient boosting and deep neural networks clinicians can now leverage large-scale datasets, including electronic health records and wearable sensor data, to predict overdose risks and individual treatment responses with high precision. These computational models facilitate early risk stratification, enabling proactive interventions before the onset of severe dependency or life-threatening events. The application of natural language processing and reinforcement learning allows for the dynamic adjustment of pharmacotherapeutic and psychosocial protocols, moving the field toward a precision medicine framework. Despite these advancements, the clinical utility of AI is currently moderated by challenges related to data heterogeneity, algorithmic transparency, and ethical considerations regarding patient privacy and bias. Effective integration into the broader healthcare ecosystem requires standardized validation protocols and collaborative efforts between data scientists and clinicians. Addressing these systemic barriers is essential for the realization of a data-driven approach to addiction recovery that reduces relapse rates and enhances long-term survivability for affected populations.

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