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Use of artificial intelligence in adolescents' mental health care: systematic scoping review of current applications and future directions

Sharma, Gauri, Yaffe, Mark J, Ghadiri, Pooria, Gandhi, Rushali, Pinkham, Laura, Gore, Genevieve, Abbasgholizadeh-Rahimi, Samira·2025·JMIR Mental Health
DOI 10.2196/70438PMC PMC12165596
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Abstract

Background: Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to decrease mortality and morbidity. Artificial intelligence’s (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health. Objective: This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents’ mental health care. Methods: We used the Arksey and O’Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Results: Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. Conclusions: In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.

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  • Sharma et al. (2025) found high or unclear risk of bias in the majority of included studies in their systematic scoping review of AI in adolescent mental health and called urgently for greater end-user involvement and more rigorous trial designs.

    “Sharma et al. (2025) reached similar conclusions in their systematic scoping review of AI in adolescent mental health, finding high or unclear risk of bias in the majority of included studies and calling urgently for greater end-user involvement and more rigorous trial designs.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Sharma et al. (2025)

  • Existing reviews of digital mental health interventions for LGBTQ+ populations have addressed the community broadly or focused on general youth populations without attending to the specific needs of sexual and gender minorities.

    “Existing reviews of digital mental health interventions for LGBTQ+ populations have addressed the community broadly (Bragazzi et al., 2023) or focused on general youth populations without attending to the specific needs of sexual and gender minorities (Liu et al., 2023;Sharma et al., 2025).”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Sharma et al., 2025

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Research works printed but not linked: 59

  1. Age limits and adolescents, Paediatr Child Health, 8, 577, 10.1093/pch/8.9.577 · DOI
  2. Murphy M Fonagy P . Mental health problems in children and young people. UK Department of Health; 2012:1-13. URL: https://assets.publishing.service.gov.uk/media/5a7c4971e5274a1b00422bf9/33571_2901304_CMO_Chapter_10.pdf [Accessed 10-10-2024]
  3. Aoki, Adolescence as a critical period for developmental plasticity, Brain Res, 1654, 85, 10.1016/j.brainres.2016.11.026 · DOI
  4. Trotman, The development of psychotic disorders in adolescence: a potential role for hormones, Horm Behav, 64, 411, 10.1016/j.yhbeh.2013.02.018 · DOI
  5. Omarov, Artificial intelligence-enabled chatbots in mental health: a systematic review, Computers, Materials & Continua, 74, 5105, 10.32604/cmc.2023.034655 · DOI
  6. Philippe, Digital health interventions for delivery of mental health care: systematic and comprehensive meta-review, JMIR Ment Health, 9, 10.2196/35159 · DOI · Paper page
  7. Wasserman, World Psychiatry, 4, 114
  8. Wyman, Developmental approach to prevent adolescent suicides: research pathways to effective upstream preventive interventions, Am J Prev Med, 47, S251, 10.1016/j.amepre.2014.05.039 · DOI · Paper page
  9. Wells, Affective disorders in children and adolescents: addressing unmet need in primary care settings, Biol Psychiatry Cogn Neurosci Neuroimaging, 49, 1111, 10.1016/S0006-3223(01)01113-1 · DOI
  10. Cockburn IM Henderson R Stern S . The impact of artificial intelligence on innovation. National Bureau of Economic Research; 2018. URL: https://www.nber.org/papers/w24449 [Accessed 05-10-2024] · DOI
  11. Fatima, National strategic artificial intelligence plans: a multi-dimensional analysis, Econ Anal Policy, 67, 10.1016/j.eap.2020.07.008 · DOI · Paper page
  12. Garbuio, Artificial intelligence as a growth engine for health care startups: emerging business models, Calif Manage Rev, 61, 59, 10.1177/0008125618811931 · DOI
  13. Rowe, Artificial intelligence for personalized preventive adolescent healthcare, J Adolesc Health, 67, S52, 10.1016/j.jadohealth.2020.02.021 · DOI
  14. Kar, Improvement of oral cancer screening quality and reach: the promise of artificial intelligence, J Oral Pathology Medicine, 49, 727, 10.1111/jop.13013 · DOI
  15. van Hartskamp, Artificial intelligence in clinical health care applications: viewpoint, Interact J Med Res, 8, 10.2196/12100 · DOI
  16. Bernert, Artificial intelligence and suicide prevention: a systematic review of machine learning investigations, Int J Environ Res Public Health, 17, 10.3390/ijerph17165929 · DOI
  17. Voss, Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder, JAMA Pediatr, 173, 446, 10.1001/jamapediatrics.2019.0285 · DOI · Paper page
  18. McGinnis, Digital phenotype for childhood internalizing disorders: less positive play and promise for a brief assessment battery, IEEE J Biomed Health Inform, 25, 3176, 10.1109/JBHI.2021.3053846 · DOI
  19. Chang, Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD, BMC Psychiatry, 21, 10.1186/s12888-021-03040-5 · DOI
  20. Li, Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents, Sci Rep, 12, 10.1038/s41598-022-15124-7 · DOI
  21. Dominitz, Gastroenterol Hepatol (Bartlesville), 17, 551
  22. Arksey, Scoping studies: towards a methodological framework, Int J Soc Res Methodol, 8, 19, 10.1080/1364557032000119616 · DOI · Paper page
  23. Levac, Scoping studies: advancing the methodology, Implement Sci, 5, 69, 10.1186/1748-5908-5-69 · DOI · Paper page
  24. Stone, Popping the (PICO) question in research and evidence-based practice, Appl Nurs Res, 15, 197, 10.1053/apnr.2002.34181 · DOI
  25. Beaujean, Simulating data for clinical research: a tutorial, J Psychoeduc Assess, 36, 7, 10.1177/0734282917690302 · DOI
  26. Ghadiri P Pinkham L Sharma G Adler PSJ Gore G Rahimi SA . AI in mental healthcare for adolescents: scoping review in family medicien forum. Presented at: Family Medicien Forum; 2021; Toronto, Canada.
  27. Ghadiri P Pinkham L Sharma G et al. AI interventions in the care of adolescents’ mental health: a systematic scoping review. Presented at: The Advancement of Artificial Intelligence (AAAI) 2021 Spring Symposium Series, Applied AI in Healthcare Symposia; Mar 22-24, 2021; Palo Alto, USA. URL: https://aaai.org/conference/spring-symposia/sss21/sss21participation/ [Accessed 05-10-2024]
  28. Oakden-Rayner L Palmer L . Docs are rocs: a simple off-the-shelf approach for estimating average human performance in diagnostic studies. arXiv. Preprint posted online on 2020. arXiv:2009.11060 · arXiv
  29. Lim, Prediction models for suicide attempts among adolescents using machine learning techniques, Clin Psychopharmacol Neurosci, 20, 609, 10.9758/cpn.2022.20.4.609 · DOI
  30. Lorge I Joyce DW Kormilitzin A . Large language models perform on par with experts identifying mental health factors in adolescent online forums. Preprint posted online on 2024.
  31. Zantvoort, Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions, NPJ Digit Med, 7, 10.1038/s41746-024-01360-w · DOI
  32. Lv, The cross-sectional study of depressive symptoms and associated factors among adolescents by backpropagation neural network, Public Health (Fairfax), 208, 10.1016/j.puhe.2022.04.017 · DOI
  33. Collins, Reporting of artificial intelligence prediction models, The Lancet, 393, 1577, 10.1016/S0140-6736(19)30037-6 · DOI · Paper page
  34. McIsaac, A novel application of a data mining technique to study intersections in the social determinants of mental health among young Canadians, SSM Popul Health, 16, 10.1016/j.ssmph.2021.100946 · DOI
  35. Mouchabac, Prevention of suicidal relapses in adolescents with a smartphone application: bayesian network analysis of a preclinical trial using in silico patient simulations, J Med Internet Res, 23, 10.2196/24560 · DOI
  36. Nagaoka, Identify adolescents’ help-seeking intention on suicide through self- and caregiver’s assessments of psychobehavioral problems: deep clustering of the Tokyo TEEN Cohort study, Lancet Reg Health West Pac, 43, 10.1016/j.lanwpc.2023.100979 · DOI
  37. Penfold, Predicting suicide attempts and suicide deaths among adolescents following outpatient visits, J Affect Disord, 294, 10.1016/j.jad.2021.06.057 · DOI
  38. Qasrawi, Assessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysis, JMIR Form Res, 6, 10.2196/32736 · DOI
  39. Qasrawi, Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments, Front Psychiatry, 14, 10.3389/fpsyt.2023.1071622 · DOI
  40. Rajapaksha, A bayesian learning model to predict the risk for cannabis use disorder, Drug Alcohol Depend, 236, 10.1016/j.drugalcdep.2022.109476 · DOI
  41. Ren, What factors are most closely associated with mood disorders in adolescents during the COVID-19 pandemic? a cross-sectional study based on 1,771 adolescents in Shandong Province, China, Front Psychiatry, 12, 10.3389/fpsyt.2021.728278 · DOI
  42. Rothenberg, Predicting adolescent mental health outcomes across cultures: a machine learning approach, J Youth Adolesc, 52, 1595, 10.1007/s10964-023-01767-w · DOI
  43. Santoso, A comparative analysis of decision tree and support vector machine on suicide ideation detection, Procedia Comput Sci, 227, 10.1016/j.procs.2023.10.553 · DOI
  44. Abbasgholizadeh Rahimi, EDAI framework for integrating equity, diversity, and inclusion throughout the lifecycle of AI to improve health and oral health care: qualitative study, J Med Internet Res, 26, 10.2196/63356 · DOI
  45. Sedgwick, Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records, BMJ Open, 13, 10.1136/bmjopen-2022-061640 · DOI
  46. Song, Exploring Korean adolescent stress on social media: a semantic network analysis, PeerJ, 11, 10.7717/peerj.15076 · DOI
  47. Stevens, Natural language processing insight into LGBTQ+ youth mental health during the COVID-19 pandemic: longitudinal content analysis of anxiety-provoking topics and trends in emotion in LGBTeens microcommunity subreddit, JMIR Public Health Surveill, 7, 10.2196/29029 · DOI · Paper page
  48. Su, Machine learning for suicide risk prediction in children and adolescents with electronic health records, Transl Psychiatry, 10, 10.1038/s41398-020-01100-0 · DOI
  49. van Vuuren, Comparing machine learning to a rule-based approach for predicting suicidal behavior among adolescents: results from a longitudinal population-based survey, J Affect Disord, 295, 10.1016/j.jad.2021.09.018 · DOI
  50. Wei, Health-behaviors associated with the growing risk of adolescent suicide attempts: a data-driven cross-sectional study, Am J Health Promot, 35, 688, 10.1177/0890117120977378 · DOI
  51. Weintraub, Word usage in spontaneous speech as a predictor of depressive symptoms among youth at high risk for mood disorders, J Affect Disord, 323, 10.1016/j.jad.2022.12.047 · DOI
  52. Weller, Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: a large-scale machine learning approach, PLoS ONE, 16, 10.1371/journal.pone.0258535 · DOI
  53. Zhang-James, Machine-learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data, J Child Psychol Psychiatry, 61, 1370, 10.1111/jcpp.13226 · DOI
  54. Zhong, A machine learning algorithm-based model for predicting the risk of non-suicidal self-injury among adolescents in western China: a multicentre cross-sectional study, J Affect Disord, 345, 10.1016/j.jad.2023.10.110 · DOI
  55. Zhou, Prediction of non-suicidal self-injury in adolescents at the family level using regression methods and machine learning, J Affect Disord, 352, 10.1016/j.jad.2024.02.039 · DOI
  56. Zhou, Identifying the risk of depression in a large sample of adolescents: an artificial neural network based on random forest, J Adolesc, 96, 1485, 10.1002/jad.12357 · DOI
  57. Bajaj, Machine learning based identification of structural brain alterations underlying suicide risk in adolescents, Discov Ment Health, 3, 10.1007/s44192-023-00033-6 · DOI
  58. Balano JB Huerto VL Sanchez S Saharkhiz A Goma JD . Determining the level of depression using BDI-II through voice recognition. Presented at: 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA); Apr 12-15, 2019:387-391; Tokyo, Japan. [doi: 10.1109/IEA.2019.8715187] · DOI
  59. Li, Analyzing and identifying teens’ stressful periods and stressor events from a microblog, IEEE J Biomed Health Inform, 21, 1434, 10.1109/JBHI.2016.2586519 · DOI

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Metadata

Authors
Sharma, G., Yaffe, M. J., Ghadiri, P., Gandhi, R., Pinkham, L., Gore, G., & Abbasgholizadeh-Rahimi, S.
ORCID
Venue
JMIR Mental Health
Publisher
JMIR Publications Inc.
Published
2025-06-06
Volume
12
Pages
e70438-e70438
ISSN
2368-7959
DOI
10.2196/70438
PMC
PMC12165596
Cited by
38
References
15
Language
en

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