Artificial intelligence is increasingly explored as an alternative approach for adolescent substance use prevention, yet it remains unclear whether existing applications demonstrate sufficient maturity, effectiveness, or public health value. We conducted a systematic review to synthesise the emerging evidence on artificial intelligence-based approaches for adolescent substance use prevention. We conducted a systematic review in line with PRISMA 2020 and SWiM guidance. We searched PubMed, Scopus, Web of Science, PsycINFO, IEEE Xplore, African Journals Online, Google, and Google Scholar from inception to August 2025. We included empirical studies that examined artificial intelligence-based approaches for adolescent substance use prevention, including risk identification and prevention-relevant engagement, among individuals aged 10 to 19 years. We extracted data on application functions, stage of development, reported outcomes, and ethical considerations. Given the diversity of study designs and outcome measures, we synthesised findings narratively. Prediction-modelling studies were assessed using PROBAST + AI. The review protocol was registered with PROSPERO (CRD420251105170). Ten studies met the inclusion criteria, spanning low-, middle-, and high-income settings. Most applications focussed on predictive modelling to identify substance use risk, while fewer evaluated user-facing conversational agents or chatbots. Across studies, systems largely remained at proof-of-concept or pilot stages. Outcome reporting was dominated by technical performance measures, feasibility assessments, and short-term engagement indicators; no study evaluated behavioural prevention outcomes, such as delayed initiation or reductions in substance use. Ethical considerations, including privacy, consent, stigma, bias, and accountability, were frequently identifiable but addressed inconsistently. Current evidence suggests that artificial intelligence in adolescent substance use prevention remains largely confined to technical feasibility, with no demonstrated effects on behavioural prevention outcomes. Future research should prioritise rigorous evaluation of prevention-relevant outcomes, embed ethics-by-design, and situate artificial intelligence applications within established prevention systems.
Atinga et al. (Sun,) studied this question.