Volume 46 Issue 10
Oct.  2025
Turn off MathJax
Article Contents
ZHANG Ludan, LI Yaoyao, LI Qiurong, LU Jinkui, NIU Zhining. Research progress on the application of artificial intelligence technology in mental health services among children and adolescents[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2025, 46(10): 1511-1515. doi: 10.16835/j.cnki.1000-9817.2025310
Citation: ZHANG Ludan, LI Yaoyao, LI Qiurong, LU Jinkui, NIU Zhining. Research progress on the application of artificial intelligence technology in mental health services among children and adolescents[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2025, 46(10): 1511-1515. doi: 10.16835/j.cnki.1000-9817.2025310

Research progress on the application of artificial intelligence technology in mental health services among children and adolescents

doi: 10.16835/j.cnki.1000-9817.2025310
  • Received Date: 2025-08-28
  • Rev Recd Date: 2025-09-23
  • Publish Date: 2025-10-25
  • The application of artificial intelligence (AI) technology in the field of mental health is becoming increasingly extensive and in-depth. As digital natives, children and adolescents are experiencing a significant shift in the prevention, identification, and intervention of their mental health issues: transitioning from traditional models to intelligent and digital approaches. At present, the mental health status of children and adolescents in China is not optimistic, with emotional disorders such as depression and anxiety showing a trend toward younger onset. However, the traditional mental health service model faces multiple challenges, including a shortage of professionals, low service accessibility, and difficulties in early identification. The study systematically reviews the current application status of AI technology in the mental health field of children and adolescents, including the latest progress in screening, assessment, intervention, and treatment; deeply analyzes the key challenges currently faced; and proposes suggestions for promoting the deep integration of AI technology and mental health services for children and adolescents, providing theoretical support and practical guidance for the intelligent, precise, and inclusive development of mental health services.
  • loading
  • [1]
    宋逸, 马军. 全面促进中国儿童青少年心理健康发展[J]. 中华流行病学杂志, 2023, 44(10): 1531-1536.

    SONG Y, MA J. Promoting the mental health of Chinese children and adolescents comprehensively[J]. Chin J Epidemiol, 2023, 44(10): 1531-1536. (in Chinese)
    [2]
    郑璐, 吕晓虹. 人工智能技术赋能大学生心理健康教育探析[J]. 锦州医科大学学报(社会科学版), 2025, 23(4): 87-89.

    ZHENG L, LV X H. Analysis of empowering college students' mental health education with artificial intelligence technology[J]. J Jinzhou Med Univ Soc Sci Ed, 2025, 23(4): 87-89. (in Chinese)
    [3]
    庞红卫, 王翠芳, 李刚, 等. 基于人工智能的学生心理健康监测与评价体系的构建[J]. 教育测量与评价, 2022(3): 31-39.

    PANG H W, WANG C F, LI G, et al. The construction of student mental health monitoring and evaluation system based on artificial intelligence[J]. Educ Meas Eval, 2022(3): 31-39. (in Chinese)
    [4]
    王伟军, 刘辉, 王玮, 等. 中小学生网络素养及其评价指标体系研究[J]. 华中师范大学学报(人文社会科学版), 2021, 60(1): 165-173.

    WANG W J, LIU H, WANG W, et al. Research on the evaluating indicators system of Internet literacy for K12 students[J]. J Cent China Norm Univ Humanit Soc Sci, 2021, 60(1): 165-173. (in Chinese)
    [5]
    MCGROW K. Artificial intelligence: essentials for nursing[J]. Nursing, 2019, 49(9): 46-49.
    [6]
    ROBERT N. How artificial intelligence is changing nursing[J]. Nurs Manage, 2019, 50(9): 30-39.
    [7]
    VON GERICH H, MOEN H, BLOCK L J, et al. Artificial Intelligence-based technologies in nursing: a scoping literature review of the evidence[J]. Int J Nurs Stud, 2022, 127: 104153.
    [8]
    MAHAMAD S, CHIN Y H, ZULMUKSAH N I N, et al. Technical review: architecting an AI-driven decision support system for enhanced online learning and assessment[J]. Future Internet, 2025, 17(9): 383.
    [9]
    VANHOOK C, ABUSUAMPEH D, POLLARD J. Leveraging generative AI to simulate mental healthcare access and utilization[J]. Front Health Serv, 2025, 5: 1654106.
    [10]
    KHAN A, LIU Q, WANG K. iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes[J]. BMC Bioinformatics, 2018, 19(Suppl 17): 501.
    [11]
    THOMPSON P M, VIDAL C, GIEDD J N, et al. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia[J]. Proc Natl Acad Sci USA, 2001, 98(20): 11650-11655.
    [12]
    KALMADY S V, GREINER R, AGRAWAL R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning[J]. NPJ Schizophr, 2019, 5(1): 2.
    [13]
    SRINIVASAN K, MAHENDRAN N, VINCENT D R, et al. Realizing an integrated multistage support vector machine model for augmented recognition of unipolar depression[J]. Electronics, 2020, 9(4): 647.
    [14]
    NICHOLS J A, HERBERT CHAN H W, BAKER M A B. Machine learning: applications of artificial intelligence to imaging and diagnosis[J]. Biophys Rev, 2019, 11(1): 111-118.
    [15]
    BOHATEREWICZ B, SOBCZAK A M, PODOLAK I, et al. Machine learning-based identification of suicidal risk in patients with schizophrenia using multi-level resting-state fMRI features[J]. Front Neurosci, 2020, 14: 605697.
    [16]
    JAVED A R, SAADIA A, MUGHAL H, et al. Artificial intelligence for cognitive health assessment: state-of-the-art, open challenges and future directions[J]. Cogn Comput, 2023, 15(6): 1767-1812.
    [17]
    ZHENG Z, ZHENG P, ZOU X. Peripheral blood S100B levels in autism spectrum disorder: a systematic review and Meta-analysis[J]. J Autism Dev Disord, 2021, 51(8): 2569-2577.
    [18]
    AI M, KUANG L. Research progress on artificial intelligence in early warning of suicide and self-harm risk in adolescents[J]. J Int Psychiatry, 2024, 51(4): 1014-1017.
    [19]
    WALSH C G, RIBEIRO J D, FRANKLIN J C. Predicting risk of suicide attempts over time through machine learning[J]. Clin Psychol Sci, 2017, 5(3): 457-469.
    [20]
    FERNANDES A C, DUTTA R, VELUPILLAI S, et al. Identifying suicide ideation and suicidal attempts in a psychiatric clinical research database using natural language processing[J]. Sci Rep, 2018, 8(1): 7426.
    [21]
    EDGCOMB J B, TSENG C H, PAN M, et al. Assessing detection of children with suicide-related emergencies: evaluation and development of computable phenotyping approaches[J]. JMIR Ment Health, 2023, 10: e47084.
    [22]
    STEWART S L, CELEBRE A, HIRDES J P, et al. Risk of suicide and self-harm in kids: the development of an algorithm to identify high-risk individuals within the children's mental health system[J]. Child Psychiatry Hum Dev, 2020, 51(6): 913-924.
    [23]
    CUMMINS N, SCHERER S, KRAJEWSKI J, et al. A review of depression and suicide risk assessment using speech analysis[J]. Speech Commun, 2015, 71: 10-49.
    [24]
    INKSTER B, SARDA S, SUBRAMANIAN V. An empathy-driven, conversational artificial intelligence agent (wysa) for digital mental well-being: real-world data evaluation mixed-methods study[J]. JMIR Mhealth Uhealth, 2018, 6(11): e12106.
    [25]
    张正, 孟芸, 王园园. 数字疗法在青少年社交焦虑障碍干预中的应用、发展与挑战[J]. 实用医学杂志, 2025, 41(10): 1439-1444.

    ZHANG Z, MENG Y, WANG Y Y. Application, development, and challenges of digital therapeutics in interventions for adolescent social anxiety disorder[J]. J Pract Med, 2025, 41(10): 1439-1444. (in Chinese)
    [26]
    FITZPATRICK K K, DARCY A, VIERHILE M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): a randomized controlled trial[J]. JMIR Ment Health, 2017, 4(2): e19.
    [27]
    GHOSH C C, MCVICAR D, DAVIDSON G, et al. What can we learn about the psychiatric diagnostic categories by analysing patients' lived experiences with machine-learning?[J]. BMC Psychiatry, 2022, 22(1): 427.
    [28]
    ABDULLAH S, MATTHEWS M, FRANK E, et al. Automatic detection of social rhythms in bipolar disorder[J]. J Am Med Inform Assoc, 2016, 23(3): 538-543.
    [29]
    CUMMINS N, MATCHAM F, KLAPPER J, et al. Artificial intelligence to aid the detection of mood disorders[M]//BARH D. Artificial intelligence in precision health. Academic Press, NewYork: Academic Press, 2020: 231-255.
    [30]
    MILLAR L, MCCONNACHIE A, MINNIS H, et al. Phase 3 diagnostic evaluation of a smart tablet serious game to identify autism in 760 children 3-5 years old in Sweden and the United Kingdom[J]. BMJ Open, 2019, 9(7): e026226.
    [31]
    王丽梅, 李仲, 古天. 人工智能赋能心理健康教育的技术基础与应用图谱[J]. 中小学信息技术教育, 2025(7): 38-40.

    WANG L M, LI Z, GU T. Technical basis and application map of mental health education with artificial intelligence empowerment[J]. Inform Technol Educ Prim Sec Sch, 2025(7): 38-40. (in Chinese)
    [32]
    POSADA J D, BARDA A J, SHI L, et al. Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records[J]. J Biomed Inform, 2017, 75S: S94-S104.
    [33]
    LEVKOVICH I, ELYOSEPH Z. Suicide risk assessments through the eyes of ChatGPT-3.5 versus ChatGPT-4: vignette study[J]. JMIR Ment Health, 2023, 10: e51232.
    [34]
    BAIN E E, SHAFNER L, WALLING D P, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia[J]. JMIR Mhealth Uhealth, 2017, 5(2): e18.
    [35]
    ATHREYA A P, VANDE VOORT J L, SHEKUNOV J, et al. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants[J]. J Child Psychol Psychiatry, 2022, 63(11): 1347-1358.
    [36]
    VAIDYAM A N, WISNIEWSKI H, HALAMKA J D, et al. Chatbots and conversational agents in mental health: a review of the psychiatric landscape[J]. Can J Psychiatry, 2019, 64(7): 456-464.
    [37]
    ORSOLINI L, POMPILI S, SALVI V, et al. A systematic review on TeleMental health in youth mental health: focus on anxiety, depression and obsessive-compulsive disorder[J]. Medicina, 2021, 57(8): 793.
    [38]
    MORTIMER R, SOMERVILLE M P, MECHLER J, et al. Connecting over the Internet: establishing the therapeutic alliance in an Internet-based treatment for depressed adolescents[J]. Clin Child Psychol Psychiatry, 2022, 27(3): 549-568.
    [39]
    KHALAF A M, ALUBIED A A, KHALAF A M, et al. The impact of social media on the mental health of adolescents and young adults: a systematic review[J]. Cureus, 2023, 15(8): e42990.
    [40]
    LEHTIMAKI S, MARTIC J, WAHL B, et al. Evidence on digital mental health interventions for adolescents and young people: systematic overview[J]. JMIR Ment Health, 2021, 8(4): e25847.
    [41]
    OPEL D J, KIOUS B M, COHEN I G. AI as a mental health therapist for adolescents[J]. JAMA Pediatr, 2023, 177(12): 1253-1254.
    [42]
    DENECKE K, ABD-ALRAZAQ A, HOUSEH M. Artificial intelligence for chatbots in mental health: opportunities and challenges[M]//HOUSEH M, BORYCKI E, KUSHNIRUK A. Multiple perspectives on artificial intelligence in healthcare. Cham: Springer International Publishing, 2021: 115-128.
    [43]
    AHMED A, HASSAN A, AZIZ S, et al. Chatbot features for anxiety and depression: a scoping review[J]. Health Informatics J, 2023, 29(1): 14604582221146719.
    [44]
    ROMAEL HAQUE M D, RUBYA S. An overview of chatbot-based mobile mental health apps: insights from app description and user reviews[J]. JMIR Mhealth Uhealth, 2023, 11: e44838.
    [45]
    GOMES P V, SÁ V J, DONGA J, et al. The use of artificial intelligence in interactive virtual reality adaptive environments with real-time biofeedback applied to phobias psychotherapy[J]. Proceedings XoveTIC, 2023, 10: 275-279.
    [46]
    CHESHAM R K, MALOUFF J M, SCHUTTE N S. Meta-analysis of the efficacy of virtual reality exposure therapy for social anxiety[J]. Behav Change, 2018, 35(3): 152-166.
    [47]
    CHANG J C, LIN H Y, LV J, et al. Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD[J]. BMC Psychiatry, 2021, 21(1): 26.
    [48]
    ACHARYA U R, OH S L, HAGIWARA Y, et al. Automated EEG-based screening of depression using deep convolutional neural network[J]. Comput Methods Programs Biomed, 2018, 161: 103-113.
    [49]
    ZOU L, ZHENG J, MIAO C, et al. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI[J]. IEEE Access, 2017, 5: 23626-23636.
    [50]
    RIAD R, DENAIS M, DE GENNES M, et al. Automated speech analysis for risk detection of depression, anxiety, insomnia, and fatigue: algorithm development and validation study[J]. J Med Internet Res, 2024, 26: e58572.
    [51]
    THAKKAR A, GUPTA A, DE SOUSA A. Artificial intelligence in positive mental health: a narrative review[J]. Front Digit Health, 2024, 6: 1280235.
    [52]
    MINERVA F, GIUBILINI A. Is AI the future of mental healthcare?[J]. Topoi (Dordr), 2023, 42(3): 1-9.
    [53]
    梁朋, 郭玲, 李秋雨. 生成式人工智能视角下大学生心理健康教育研究[J]. 佛山科学技术学院学报(社会科学版), 2024, 42(4): 96-100.

    LIANG P, GUO L, LI Q Y. Research on mental health education for college students from the perspective of generative artificial intelligence[J]. J Foshan Univ Soc Sci Ed, 2024, 42(4): 96-100. (in Chinese)
    [54]
    MIOTTO R, WANG F, WANG S, et al. Deep learning for healthcare: review, opportunities and challenges[J]. Brief Bioinform, 2018, 19(6): 1236-1246.
    [55]
    INIESTA R, STAHL D, MCGUFFIN P. Machine learning, statistical learning and the future of biological research in psychiatry[J]. Psychol Med, 2016, 46(12): 2455-2465.
    [56]
    朱廷劭. 试析通用人工智能在心理学领域的应用[J]. 人民论坛·学术前沿, 2023(14): 86-91, 101.

    ZHU T S. An analysis of the application of AGI in the field of psychology[J]. Frontiers, 2023(14): 86-91, 101. (in Chinese)
    [57]
    DICUONZO G, DONOFRIO F, FUSCO A, et al. Healthcare system: moving forward with artificial intelligence[J]. Technovation, 2023, 120: 102510.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (8) PDF downloads(0) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return