From the streets to stability: An AI-driven step-by-step framework for rehabilitating drug-dependent individuals

Drug addiction remains a critical public health issue, requiring innovative approaches to rehabilitation. This study proposes an AI-driven, step-by-step framework designed to enhance the rehabilitation of drug-dependent individuals by integrating predictive analytics, personalized interventions, and long-term support systems. The framework is structured into five key stages: AI-assisted screening and assessment; personalized treatment planning; real-time monitoring and adaptive intervention; relapse prediction and prevention; and long-term recovery support. Through advanced machine learning algorithms, AI can analyze behavioural, physiological, and environmental data to customize treatment plans and provide continuous patient monitoring. Predictive modeling enhances early relapse detection, allowing timely interventions to prevent setbacks. Additionally, AI-powered recovery applications and virtual support networks facilitate long-term stability by ensuring sustained engagement with treatment resources. This paper finds that by improving accessibility, scalability, and the consistency of rehabilitation programs, AI presents a transformative solution to overcoming existing barriers in addiction treatment. This framework underscores the potential of AI to revolutionize drug rehabilitation, offering a data-driven approach to achieving lasting recovery outcomes.