The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Integrating Artificial Intelligence in Youth Mental Health Care: Advances, Challenges, and Future Directions
Abstract
Purpose: Youth mental health challenges are on the rise. Despite significant efforts to expand access, traditional approaches are unable to keep pace with the growing demand. While Artificial Intelligence (AI) has emerged as a promising solution to bridge the needs-provision gap, most clinicians remain unaware of the expanding range of AI tools. To support a clearer understanding of the field, this practitioner-focused commentary uses illustrative examples to explore current and future applications of AI in youth mental healthcare; address key challenges in adoption; and provide a practical framework for safe and effective integration into clinical practice. Recent Findings: AI is becoming increasingly embedded in youth mental health care, with applications spanning early detection, diagnostic decision-making, treatment delivery, clinician training, and research acceleration. While these technologies hold significant promise, many remain insufficiently validated. For youth populations, regulatory gaps, algorithmic biases, digital inequities, and the potential for technological overdependence present distinct barriers to widespread adoption. Summary: AI is the future of youth mental health care. However, given the nascency of the field and the rapid proliferation of new tools, these technologies should be approached with thoughtful caution. To ensure safe and effective integration, clinicians are advised to use AI tools only within their area of expertise, apply them strictly within their defined therapeutic scope, and ensure alignment with legal and professional standards. Crucially, AI should serve as a complement – not a replacement – for traditional care, and may be unsuitable for youth with severe, complex, or rare clinical profiles.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Marshall et al. (2025) echoed these concerns, noting that while AI applications in youth mental health are advancing rapidly, the evidence base has not kept pace with the technology.”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Marshall et al. (2025)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 1×
“Applications like Wysa (UK) [20] and Woebot (US) [21] offer on-demand, text-based therapeutic interactions, including AI-guided journaling, mindfulness exercises, and live-text therapy sessions.”
Adolescence as a sensitive period of brain development
Fuhrmann, D., Knoll, L. J., & Blakemore, S.-J. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.008 · Cited by 1,158
Inkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063
The Lancet Psychiatry Commission on youth mental health
McGorry, P. D., Mei, C., Dalal, N., Alvarez-Jimenez, M., Blakemore, S.-J., Browne, V., Dooley, B., Hickie, I. B., Jones, P. B., McDaid, D., et al. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(24)00163-9 · Cited by 663
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598 · cited 1×
“Conversational agents are also being used to provide mental health support [19].”
Szuhany, K. L., & Simon, N. M. (2022) · JAMA · doi:10.1001/jama.2022.22744 · Cited by 541
Natural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476
Zhou, X., Teng, T., Zhang, Y., Del Giovane, C., Furukawa, T. A., Weisz, J. R., Li, X., Cuijpers, P., Coghill, D., Xiang, Y., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30137-1 · Cited by 367
Considerations for addressing bias in artificial intelligence for health equity
Abràmoff, M. D., Tarver, M. E., Loyo-Berrios, N., Trujillo, S., Char, D., Obermeyer, Z., Eydelman, M. B., Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, W., & Maisel, W. H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00913-9 · Cited by 349
Tools to support the automation of systematic reviews: a scoping review.
Khalil, H., Ameen, D., & Zarnegar, A. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.12.005 · Cited by 190 · cited 1×
“For systematic reviews and other aggregate research, specialized AI tools can automate key stages of the review process, including screening (e.g. Rayaan ), data extraction (e.g. ExaCT ), and evidence synthesis (e.g. NetMetaXL ) [34, 35].”
Research works printed but not linked: 34
- McGorry PD, Mei C, Chanen A, Hodges C, Alvarez-Jimenez M, Killackey E. Designing and scaling up integrated youth mental health care. World Psychiatry. 2022. https://doi.org/10.1002/wps.20938. · DOI · Paper page
- Pretorius C, Chambers D, Coyle D. Young people’s online help-seeking and mental health difficulties: Systematic narrative review. J Med Internet Res. 2019. https://doi.org/10.2196/13873. · DOI · Paper page
- Wang S, Li Q, Lu J, Ran H, Che Y, Fang D, Liang X, Sun H, Chen L, Peng J, Shi Y. Treatment rates for mental disorders among children and adolescents: A systematic review and meta-analysis. JAMA Netw Open. 2023. https://doi.org/10.1001/jamanetworkopen.2023.38174. · DOI · Paper page
- Yim IH, Su J. Artificial intelligence (AI) learning tools in K-12 education: A scoping review. J Comput Education. 2024. https://doi.org/10.1007/s40692-023-00304-9. · DOI · Paper page
- Kumbure MM, Lohrmann C, Luukka P, Porras J. Machine learning techniques and data for stock market forecasting: A literature review. Expert Syst Appl. 2022. https://doi.org/10.1016/j.eswa.2022.116659. · DOI
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer. 2018. https://doi.org/10.1038/s41568-018-0016-5. · DOI · Paper page
- Physician sentiments around the use of AI in heath care: motivations, opportunities, risks, and use cases. In: AMA Augmented Intelligence Research. American Medical Association. 2025; https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf. Accessed 25 Feb 2025.
- Misra R, Keane PA, Hogg HD. How should we train clinicians for artificial intelligence in healthcare? Future Healthcare J. 2024. https://doi.org/10.1016/j.fhj.2024.100162. · DOI
- McGorry PD, Mei C. Early intervention in youth mental health: progress and future directions. BMJ Ment Health. 2018;21:182–4. https://doi.org/10.1136/ebmental-2018-300060. · DOI · Paper page
- Yeh PL, Kuo WC, Tseng BL, Sung YH. Does the AI-driven Chatbot Work? Effectiveness of the Woebot app in reducing anxiety and depression in group counseling courses and student acceptance of technological aids. Curr Psychol. 2025. https://doi.org/10.1007/s12144-025-07359-0. · DOI · Paper page
- Darcy A, Beaudette A, Chiauzzi E, Daniels J, Goodwin K, Mariano TY, Wicks P, Robinson A. Anatomy of a Woebot®(WB001): agent guided CBT for women with postpartum depression. Expert Rev Med Devices. 2023. https://doi.org/10.1080/17434440.2023.2280686. · DOI
- Gupta M, Malik T, Sinha C. Delivery of a mental health intervention for chronic pain through an artificial intelligence–enabled app (Wysa): protocol for a prospective pilot study. JMIR Res Protocols. 2022. https://doi.org/10.2196/36910. · DOI
- Hussain S, Ameri Sianaki O, Ababneh N. A survey on conversational agents/chatbots classification and design techniques. In: Barolli, L, Takizawa M, Xhafa F, Enokido T. Proceedings of the workshops of the 33rd international conference on advanced information networking and applications. 2019; https://doi.org/10.1007/978-3-030-15035-8_93. · DOI · Paper page
- Siegert I, Busch M, Metzner S, Krüger J. Voice Assistants for therapeutic support–a literature review. In: International conference on human-computer interaction. 2023; https://doi.org/10.1109/ICCES63552.2024.10859531. · DOI
- Jang B, Yuh C, Lee H, Shin YB, Lee HJ, Kang EK, Heo J, Cho CH. Exploring user experience and the therapeutic relationship of short-term avatar-based psychotherapy: Qualitative pilot study. JMIR Hum Factors. 2025. https://doi.org/10.2196/66158. · DOI
- Van Dellen E. Precision psychiatry: predicting predictability. Psychol Med. 2024. https://doi.org/10.1017/S0033291724000370. · DOI
- Chang JC, Lin HY, Lv J, Tseng WY, Gau SS. Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD. BMC Psychiatry. 2021. https://doi.org/10.1186/s12888-021-03040-5. · DOI
- Ball TM, Stein MB, Ramsawh HJ, Campbell-Sills L, Paulus MP. Single-subject anxiety treatment outcome prediction using functional neuroimaging. Neuropsychopharmacology. 2014. https://doi.org/10.1038/npp.2013.328. · DOI
- Elendu C, Amaechi DC, Okatta AU, Amaechi EC, Elendu TC, Ezeh CP, Elendu ID. The impact of simulation-based training in medical education: A review. Medicine. 2024. https://doi.org/10.1097/MD.0000000000038813. · DOI · Paper page
- Chen X, Liu Z, Wei L, Yan J, Hao T, Ding R. A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017. BMC Med Inform Decis Mak. 2018. https://doi.org/10.1186/s12911-018-0692-9. · DOI
- Hitch D. Artificial intelligence augmented qualitative analysis: the way of the future? Qual Health Res. 2024. https://doi.org/10.1177/10497323231217392. · DOI
- Blaizot A, Veettil SK, Saidoung P, Moreno-Garcia CF, Wiratunga N, Aceves-Martins M, Lai NM, Chaiyakunapruk N. Using artificial intelligence methods for systematic review in health sciences: A systematic review. Res Synthesis Methods. 2022;13(3):353–62. https://doi.org/10.1002/jrsm.1553. · DOI · Paper page
- Schmidt L, Sinyor M, Webb RT, Marshall C, Knipe D, Eyles EC, John A, Gunnell D, Higgins JP. A narrative review of recent tools and innovations toward automating living systematic reviews and evidence syntheses. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen. 2023; https://doi.org/10.1016/j.zefq.2023.06.007. · DOI · Paper page
- De Freitas J, Cohen IG. The health risks of generative AI-based wellness apps. Nat Med. 2024. https://doi.org/10.1038/s41591-024-02943-6. · DOI · Paper page
- Kasperbauer TJ, Wright DE. Expanded FDA regulation of health and wellness apps. Bioethics. 2020;34(3):235–41. https://doi.org/10.1111/bioe.12674. · DOI · Paper page
- Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digital Med. 2023. https://doi.org/10.1038/s41746-023-00873-0. · DOI · Paper page
- Warraich HJ, Tazbaz T, Califf RM. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA. 2025. https://doi.org/10.1001/jama.2024.21451. · DOI · Paper page
- Park S, Humphry J. Exclusion by design: intersections of social, digital and data exclusion. Inform Commun Soc. 2019; https://doi.org/10.1080/1369118X.2019.1606266. · DOI
- Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns. 2021. https://doi.org/10.1016/j.patter.2021.100347. · DOI · Paper page
- Notara V, Vagka E, Gnardellis C, Lagiou A. The emerging phenomenon of nomophobia in young adults: a systematic review study. Addiction Health. 2021; https://doi.org/10.22122/ahj.v13i2.309. · DOI
- Kim HS, Son G, Roh EB, Ahn WY, Kim J, Shin SH, Chey J, Choi KH. Prevalence of gaming disorder: A meta-analysis. Addict Behav. 2022. https://doi.org/10.1016/j.addbeh.2021.107183. · DOI
- Nonaka S, Takeda T, Sakai M. Who are hikikomori? Demographic and clinical features of hikikomori (prolonged social withdrawal): A systematic review. Aust N Z J Psychiatry. 2022. https://doi.org/10.1177/00048674221085917. · DOI
- Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020. https://doi.org/10.2471/BLT.19.237487. · DOI · Paper page
- Baldoni F. A dynamic-maturational model (DMM) Approach to psychotherapy, parental care and forensic assessment. J Forensic Sci Criminal Investigation. 2024; https://doi.org/10.19080/JFSCI.2024.18.555985. · DOI
Cited by
Sorted by citations
Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review
Shekarchi, R., Eleuteri, S., Saladino, V., Calaresi, D., Giordano, F., & Prunas, A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.2002054
Metadata
- Authors
- Marshall, N. J., Loades, M. E., Jacobs, C., Biddle, L., & Lambert, J. D.
- Venue
- Current Treatment Options in Psychiatry
- Published
- 2025-04-10
- Volume
- 12
- Issue
- 1
- ISSN
- 2196-3061
- Cited by
- 11
- References
- 14
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review