Utilizing Artificial Intelligence Models for Early Detection and Personalized Intervention Strategies in Children with Attention-Deficit/Hyperactivity Disorder: A Narrative Review
DOI:
https://doi.org/10.61919/m6mr9706Keywords:
Attention-Deficit/Hyperactivity Disorder; Artificial Intelligence; Machine Learning; Pediatric; Digital Health; Cognitive Assessment; Personalized Medicine.Abstract
Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition characterized by heterogeneous clinical presentation and reliance on subjective diagnostic approaches. Emerging artificial intelligence (AI) models offer potential to improve diagnostic accuracy and support individualized care by integrating multidimensional data sources. Objective: To synthesize current evidence on the application of AI models for early detection of ADHD and their potential role in supporting personalized intervention strategies in pediatric populations through a narrative review approach. Methods: A narrative review was conducted using literature from PubMed, Scopus, Web of Science, and Google Scholar published between 2020 and 2026. Studies examining AI applications in ADHD-related assessment, prediction, monitoring, or intervention were included. Evidence was synthesized thematically across data modalities, model types, clinical applications, and translational considerations. Results: The evidence base was predominantly concentrated in conceptual development (n=12) and diagnostic support (n=9), with fewer studies addressing intervention personalization (n=5). Multimodal AI approaches integrating behavioral, cognitive, and digital data demonstrated greater potential than single-source models. Wearable and ecological monitoring methods showed promise for real-time assessment, while EEG and MRI approaches provided biologically informed insights but faced implementation barriers. Intervention-oriented AI applications, including digital therapeutics and adaptive systems, remain in early stages of development. Conclusion: AI models show strong potential as supportive tools for early ADHD detection and emerging applications in personalized intervention planning. However, current evidence favors diagnostic augmentation over clinical implementation. Future research should prioritize interpretability, validation, and integration into real-world pediatric care pathways.
References
1. Leprevost CMJ. The use of artificial intelligence tools in the elucidation of cases of neurodevelopmental disorders. Arq Neuropsiquiatr. 2023;81(S1):A052.
2. Nasrallah M, El Moghrabi A, Kayal M, Matar D, Alwan I, Fakhoury M. The emerging role of artificial intelligence in the diagnosis and treatment of autism spectrum disorder and attention-deficit/hyperactivity disorder. Int J Dev Disabil. 2025;71(1):1–14.
3. Kim WP, Kim HJ, Pack SP, Lim JH, Cho CH, Lee HJ. Machine learning–based prediction of attention-deficit/hyperactivity disorder and sleep problems with wearable data in children. J Neurol Sci. 2023;6(3):e233502.
4. Sun B, Cai F, Huang H, Li B, Wei B. Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review. Expert Rev Med. 2025;250:10238.
5. Patil PY, Sarode BS, Chavan PV, Goje NS, Williams I. Prediction of attention-deficit and hyperactivity disorder in online learning. In: Intelligent Solutions for Cognitive Disorders. IGI Global; 2024. p. 133–157.
6. Zaheer A, Akhtar AJ. Artificial intelligence as a support to diagnose ADHD: an insight of unorthodox approaches: a scoping review. Clin Neuropsychol. 2025;39(1):1–35.
7. de Lacy N, Ramshaw MJ, McCauley E, Kerr KF, Kaufman J, Kutz JN. Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence. Transl Psychiatry. 2023;13(1):314.
8. Priya MG, Kalyanaraman P. Evaluation of ADHD classification and treatment strategies using machine learning. J Technol AI. 2025;103(8):1–10.
9. Hu H. Leveraging machine learning in data analysis and management for special education. Educ Technol Rev. 2024;12(2):45–60.
10. Navarro-Soria I, Rico-Juan JR, Juarez-Ruiz R, Lavigne-Cervan R. Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence. Appl Neurocomput. 2025;14(4):474–487.
11. Aylward BS, Abbas H, Taraman S, Salomon C, Gal-Szabo D, Kraft C. An introduction to artificial intelligence in developmental and behavioral pediatrics. J Dev Behav Pediatr. 2023;44(2):e126–e134.
12. Begum Z, Shaik K. Exploring diverse approaches for detecting and diagnosing attention deficit hyperactivity disorder: a comprehensive survey. Methods Eng Pract. 2024;11(11):1–15.
13. Isaev DY. Use of machine learning and computer vision methods for building behavioral and electrophysiological biomarkers. Duke Univ; 2023.
14. Kaur A, Singh K, Kaur P, Priya B, Kumar G, Sharma A. Smart health monitoring approach to diagnose ADHD based on real-time activity and heart rate variability. Res Artif Intell Comput Sci. 2024;5(2):45–60.
15. Beg MJ, Verma MK. Exploring digital and AI-driven psychotherapy for ADHD and related disorders. Int J Psychiatry Med. 2024;59(3):1–15.
16. Rasi A. Classification of ADHD using machine learning and EEG data: a systematic review. Univ Tasmania; 2024.
17. Singh RZ, Panchal J, Ali S, Krone B, Wert I, Owens M. Ecological momentary assessment of emotional dysregulation in ADHD youth. Front Psychiatry. 2025;4:1549220.
18. Gongor F, Tutsoy O. Survey on recent artificial intelligence-based assistive robotics technologies for children with special needs. Robotics Rev. 2024;8(1):1–12.
19. Zhang-James Y, Razavi AS, Hoogman M, Franke B, Faraone SV. Machine learning and MRI-based diagnostic models for ADHD. J Atten Disord. 2023;27(4):335–353.
20. Rostami M, Abdi M, Asadi NZ. Predicting the future of mental health interventions: the role of digital therapeutics. J Health Gov. 2025;2(2):33–51.
21. Wang Y, Zhao Y, Hu L, Zhang X. Multidimensional dynamic feedback model for ADHD management. Children. 2025;12(3):303.
22. Dev P, Pathak A. Deep learning in mental illnesses: understanding networks. CRC Press; 2024.
23. Mehta P, Chillarge GR, Sapkal SD, Shinde GR, Kshirsagar PS. AI-assisted special education for students with exceptional needs. IGI Global; 2023.
24. Olaniyan OT, Adetunji CO, Dare A, Adeyomoye O, Adeniyi MJ, Enoch A. Cognitive therapy for brain diseases using artificial intelligence models. Elsevier; 2023.
25. Banire B, Al Thani D, Qaraqe M. Detecting attention in children with autism using machine learning. Univers Access Inf Soc. 2024;34(2):259–291.
26. Mitsea E, Drigas A, Skianis C. A systematic review of serious games in the era of artificial intelligence. Educ Sci. 2025;14(4):649.
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Copyright (c) 2026 Tania Sehar Joseph, Bushra Shabbir, Nabiha Imran, Jahanzaib Lashari, Benish William, Arfa Tabassum (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
