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Leveraging Artificial Intelligence for Substance Use Prevention Among Adolescents: A Systematic Review of Emerging Evidence.

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Title: Leveraging Artificial Intelligence for Substance Use Prevention Among Adolescents: A Systematic Review of Emerging Evidence.
Authors: Atinga, Roger A.1 (AUTHOR), Nyarko, Simon2 (AUTHOR) nyarkosimon761@gmail.com, Akoriyea, Samuel Kaba3 (AUTHOR)
Source: Inquiry (00469580). 3/24/2026, Vol. 63, p1-15. 15p.
Subject Terms: *Artificial intelligence, *Adolescence, Substance abuse prevention, Prediction models, Descriptive statistics, Systematic reviews, MEDLINE, Ethics, Technology, Online information services, Data analysis software, Psychology information storage & retrieval systems
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. [ABSTRACT FROM AUTHOR]
Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Leveraging Artificial Intelligence for Substance Use Prevention Among Adolescents: A Systematic Review of Emerging Evidence.
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  Data: <searchLink fieldCode="JN" term="%22Inquiry+%2800469580%29%22">Inquiry (00469580)</searchLink>. 3/24/2026, Vol. 63, p1-15. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Adolescence%22">Adolescence</searchLink><br /><searchLink fieldCode="DE" term="%22Substance+abuse+prevention%22">Substance abuse prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Systematic+reviews%22">Systematic reviews</searchLink><br /><searchLink fieldCode="DE" term="%22MEDLINE%22">MEDLINE</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Online+information+services%22">Online information services</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+information+storage+%26+retrieval+systems%22">Psychology information storage & retrieval systems</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/00469580261433854
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        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Adolescence
        Type: general
      – SubjectFull: Substance abuse prevention
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Descriptive statistics
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      – SubjectFull: Systematic reviews
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      – SubjectFull: Ethics
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      – SubjectFull: Online information services
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      – SubjectFull: Psychology information storage & retrieval systems
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      – TitleFull: Leveraging Artificial Intelligence for Substance Use Prevention Among Adolescents: A Systematic Review of Emerging Evidence.
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              Text: 3/24/2026
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