AI-Powered Evaluation Tool for Addiction Rehabilitation: A Comprehensive Review

Resumen

Addiction rehabilitation is a critical global health issue, often hindered by stigma, accessibility challenges, and a lack of personalized care. Artificial intelligence (AI) offers transformative potential through innovations such as conversational AI, predictive modeling, digital therapeutics. This review synthesizes findings from over 17 peer-reviewed studies published between 2015 and 2024, focusing on AI-driven conversational assistants, adaptive treatment plans, behavioral notifications, and community-driven platforms. This AI-driven evaluation tool is designed to personalize and streamline the rehabilitation intake process for individuals battling alcohol and cigarette addiction. Using advanced large language models (LLMs), the system engages users through a conversational virtual assistant to gather crucial information about their addiction history, mental health, triggers, and support systems. The tool then generates detailed medical reports with personalized treatment plans, fitness guidance, dietary recommendations, and addiction-specific precautions. It aims to reduce intimidation often associated with traditional rehabilitation methods, enhance accessibility to treatment, and improve outcomes through real-time, personalized support. By incorporating natural language processing (NLP) and machine learning techniques, the system ensures a comprehensive and empathetic assessment, contributing to more effective and individualized recovery plans. Key contributions include multilingual conversation systems, real-time intervention, and care models for personalized care. However, challenges such as data privacy, ethics, and scalability remain. Through the examination of interlinks and gaps in current research, the contribution of AI in enhancing addiction care is emphasized, and future research priorities are set.