AI Enhanced Tai Chi Rehabilitation for Substance Use Disorder with Clinical Evidence and Predictive Modeling for Relapse Prevention

Substance use disorder (SUD) remains a significant global health issue, with relapse rates exceeding 60% in some compulsory rehabilitation centers despite structured interventions. Recent research suggests that mind–body exercises, like Tai Chi, can lessen cravings and enhance psychological well-being, though their current use is often standardized, subjective, and not personalized. This study aimed to evaluate the clinical benefits of Tai Chi within a compulsory rehabilitation setting and to develop an AI-enhanced framework for individualized relapse prevention. A randomized controlled trial (RCT) involving 168 participants diagnosed with SUD compared a Tai Chi intervention group with a standard physical education control group. Psychometric outcomes were measured using validated scales, while a conceptual AI framework using a CNN-LSTM architecture was simulated with multimodal inputs that combined psychometric indicators and motion-based features. The RCT showed significant reductions in craving, depression, and anxiety, along with improved self-control in the Tai Chi group compared to controls. Mediation analysis indicated that psychological symptoms partially explained the link between craving and relapse risk. The AI simulation achieved 82% accuracy and an area under the ROC curve of 0.85, with craving and depression identified as key predictors. These results provide initial evidence that integrating Tai Chi with AI-driven monitoring can transform exercise-based rehabilitation into a closed-loop, adaptive system capable of delivering personalized feedback and early relapse warnings. This combined approach has the potential to enhance the scalability, accuracy, and effectiveness of institutional rehab programs for SUD.