SynthesisInquiry : a journal of medical care organization, provision and financing
Leveraging Artificial Intelligence for Substance Use Prevention Among Adolescents: A Systematic Review of Emerging Evidence.
Synthesis in Inquiry : a journal of medical care organization, provision and financing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.