Journal of addictive diseases · 2026
Tailored psychotherapy and AI-enhanced contingency management for co-occurring disorders in cannabis use disorder: a systematic review.
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Mishra Sidharth, Mishra Sayali, Rath Sibanarayan
Аннотация
BACKGROUND: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hyperactivity disorder (ADHD), resulting in poorer outcomes and underscoring the need for tailored interventions. Contingency management (CM) is one of the most effective behavioral treatments for substance use disorders, and emerging applications of artificial intelligence (AI) may enhance CM by predicting relapse risk and personalizing incentives. This review evaluates evidence on integrated interventions for CUD with co-occurring disorders and the developing role of AI-enhanced CM.
METHODS: A systematic search of PubMed, PsycINFO, Embase, and Web of Science (through October 2025) identified clinical studies and systematic reviews on tailored interventions for CUD with depression, PTSD, anxiety, or ADHD, as well as research on AI-driven CM. Data were extracted on study design, interventions, and outcomes.
RESULTS: Thirty-eight studies met inclusion criteria. Integrated cognitive-behavioral therapies improved psychiatric symptoms and reduced cannabis use, particularly in depression and PTSD. Pharmacotherapies showed inconsistent benefits, while ADHD-focused behavioral and stimulant-based approaches demonstrated promising reductions in cannabis use. AI applications such as machine-learning prediction of relapse using smartphone/sensor data, remote CM delivery, and reinforcement-learning-based incentive optimization-improved attendance, abstinence verification, and reward efficiency.
CONCLUSION: Integrated psychosocial approaches and AI-augmented CM offer complementary pathways for improving outcomes in individuals with CUD and co-occurring disorders. Combining personalized psychotherapy with adaptive, technology-assisted reinforcement may strengthen treatment efficacy.
METHODS: A systematic search of PubMed, PsycINFO, Embase, and Web of Science (through October 2025) identified clinical studies and systematic reviews on tailored interventions for CUD with depression, PTSD, anxiety, or ADHD, as well as research on AI-driven CM. Data were extracted on study design, interventions, and outcomes.
RESULTS: Thirty-eight studies met inclusion criteria. Integrated cognitive-behavioral therapies improved psychiatric symptoms and reduced cannabis use, particularly in depression and PTSD. Pharmacotherapies showed inconsistent benefits, while ADHD-focused behavioral and stimulant-based approaches demonstrated promising reductions in cannabis use. AI applications such as machine-learning prediction of relapse using smartphone/sensor data, remote CM delivery, and reinforcement-learning-based incentive optimization-improved attendance, abstinence verification, and reward efficiency.
CONCLUSION: Integrated psychosocial approaches and AI-augmented CM offer complementary pathways for improving outcomes in individuals with CUD and co-occurring disorders. Combining personalized psychotherapy with adaptive, technology-assisted reinforcement may strengthen treatment efficacy.