Use Artificial Intelligence in Autism Spectrum Disorder: a comprehensive review in assessment, intervention, and data collection
DOI:
https://doi.org/10.70715/jitcai.2025.v2.i2.013Keywords:
Autism Spectrum Disorder (ASD), Artificial Intelligence, Applied Behaviour Analysis (ABA), Assessment, Intervention, Data Collection, EthicsAbstract
Autism Spectrum Disorder (ASD) presents varied challenges across assessment, intervention, and behavioural monitoring. With recent advancements in Artificial Intelligence (AI), introduced tools have emerged that support clinicians, educators, and families in understanding and managing ASD more effectively. Significant innovations include explainable AI, mobile applications, computer vision, and wearable devices, which establish promising potential to increase accuracy, accessibility, and consistency of care. This review produces findings from recent literature to examine how AI contributes to the ASD care process. It highlights the benefits of AI while noting ethical and practical considerations. Also addresses challenges in data security, privacy, and ethical AI deployment within ASD care settings.
Downloads
References
References
1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. DOI: https://doi.org/10.1176/appi.books.9780890425596
2. Vivanti, G., Bent, C., Capes, K., Upson, S., Hudry, K., Dissanayake, C., & Victorian ASELCC Team. (2022). Characteristics of children on the autism spectrum who benefit the most from receiving intervention in inclusive versus specialised early childhood education settings. Autism Research, 15(11), 2200-2209. DOI: https://doi.org/10.1002/aur.2815
3. Whitehouse, A. J., Varcin, K. J., Pillar, S., Billingham, W., Alvares, G. A., Barbaro, J., ... & Hudry, K. (2021). Effect of preemptive intervention on developmental outcomes among infants showing early signs of autism: A randomized clinical trial of outcomes to diagnosis. JAMA pediatrics, 175(11), e213298-e213298. DOI: https://doi.org/10.1001/jamapediatrics.2021.3298
4. Howes, A. E., Burns, M. E., & Surtees, A. D. (2021). Barriers, facilitators, and experiences of the autism assessment process: A systematic review of qualitative research with health professionals. Professional Psychology: Research and Practice, 52(5), 449. DOI: https://doi.org/10.1037/pro0000413
5. Ghafghazi, S., Carnett, A., Neely, L., Das, A., & Rad, P. (2021). AI-augmented behavior analysis for children with developmental disabilities: building toward precision treatment. IEEE Systems, Man, and Cybernetics Magazine, 7(4), 4-12. DOI: https://doi.org/10.1109/MSMC.2021.3086989
6. Bone, D., Bishop, S. L., Black, M. P., Goodwin, M. S., Lord, C., & Narayanan, S. S. (2016). Use of machine learning to improve autism screening and diagnostic instruments: Effectiveness, efficiency, and multi‐instrument fusion. Journal of Child Psychology and Psychiatry, 57(8), 927-937. DOI: https://doi.org/10.1111/jcpp.12559
7. Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D. B., Kacprowski, T., List, M., Matschinske, J., ... & Baumbach, J. (2022). Privacy-preserving artificial intelligence techniques in biomedicine. Methods of information in medicine, 61(S 01), e12-e27. DOI: https://doi.org/10.1055/s-0041-1740630
8. Shahamiri, S. R., Thabtah, F., & Abdelhamid, N. (2022). A new classification system for autism based on machine learning of artificial intelligence. Technology and Health Care, 30(3), 605-622. DOI: https://doi.org/10.3233/THC-213032
9. Olugbami, O. O., Ogundeko, O., Lawan, M., & Foster, E. (2025). Harnessing data for impact: Transforming public health interventions through evidence-based decision-making. World Journal of Advanced Research and Reviews, 25(1), 2085-2103. DOI: https://doi.org/10.30574/wjarr.2025.25.1.0297
10. Bhatt, P., Sethi, A., Tasgaonkar, V., Shroff, J., Pendharkar, I., Desai, A., ... & Jain, N. K. (2023). Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions. Brain informatics, 10(1), 18. DOI: https://doi.org/10.1186/s40708-023-00196-6
11. Chaddad, A., Li, J., Lu, Q., Li, Y., Okuwobi, I. P., Tanougast, C., ... & Niazi, T. (2021). Can autism be diagnosed with artificial intelligence? A narrative review. Diagnostics, 11(11), 2032. DOI: https://doi.org/10.3390/diagnostics11112032
12. Cavus, N., Lawan, A. A., Ibrahim, Z., Dahiru, A., Tahir, S., Abdulrazak, U. I., & Hussaini, A. (2021). A systematic literature review on the application of machine-learning models in behavioral assessment of autism spectrum disorder. Journal of Personalized Medicine, 11(4), 299. DOI: https://doi.org/10.3390/jpm11040299
13. Rane, N., Choudhary, S., & Rane, J. (2023). Explainable artificial intelligence (XAI) in healthcare: interpretable models for clinical decision support. Available at SSRN 4637897. DOI: https://doi.org/10.2139/ssrn.4637897
14. Al Hamieli, M., & Habbal, F. (2024). Developing an AI-Driven Mobile Application for Early Autism Diagnosis and Classification. International Journal For Autism Challenges & Solution, 1(2), 63-81. DOI: https://doi.org/10.54878/dqbj6h14
15. Samad, M. D., Diawara, N., Bobzien, J. L., Harrington, J. W., Witherow, M. A., & Iftekharuddin, K. M. (2017). A feasibility study of autism behavioral markers in spontaneous facial, visual, and hand movement response data. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(2), 353-361. DOI: https://doi.org/10.1109/TNSRE.2017.2768482
16. Deng, A., Yang, T., Chen, C., Chen, Q., Neely, L., & Oyama, S. (2024). Language-assisted deep learning for autistic behaviors recognition. Smart Health, 32, 100444. DOI: https://doi.org/10.1016/j.smhl.2023.100444
17. World Health Organization. (2021). Global strategy on digital health 2020–2025. WHO. https://www.who.int/publications/i/item/9789240020924
18. El-Jardali, F., Bou-Karroum, L., Jabbour, M., Bou-Karroum, K., Aoun, A., Salameh, S., ... & Sinha, C. (2023). Digital health in fragile states in the Middle East and North Africa (MENA) region: a scoping review of the literature. PloS one, 18(4), e0285226. DOI: https://doi.org/10.1371/journal.pone.0285226
19. Maspul, K. A., & Ardhin, M. (2025). AI Empowers Autism Nutritional Care Solutions: A Family-Centred Innovation. Health & Medical Sciences, 2(3), 19-19. DOI: https://doi.org/10.47134/phms.v2i3.418
20. Kohli, M., Kar, A. K., Bangalore, A., & Ap, P. (2022). Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. Brain Informatics, 9(1), 16. DOI: https://doi.org/10.1186/s40708-022-00164-6
21. Jayousi, Sara, Alessio Martinelli, Paolo Lucattini, and Lorenzo Mucchi. "ICT framework for supporting applied behavior analysis in the social inclusion of children with neurodevelopmental disorders." Sensors 23, no. 15 (2023): 6914. DOI: https://doi.org/10.3390/s23156914
22. Lan, L., Li, K., & Li, D. (2025). Exploring the application of AI in the education of children with autism: a public health perspective. Frontiers in Psychiatry, 15, 1521926. DOI: https://doi.org/10.3389/fpsyt.2024.1521926
23. Ahmed, S., Rahman, M. S., Kaiser, M. S., & Hosen, A. S. M. (2025). Advancing Personalized and Inclusive Education for Students with Disability Through Artificial Intelligence: Perspectives, Challenges, and Opportunities. Digital, 5(2). DOI: https://doi.org/10.3390/digital5020011
24. Lee, J., Lim, J., Kang, S., Kim, S., Jung, S. Y., Hong, S. B., & Park, Y. R. (2024). Mobile App–Assisted Parent Training Intervention for Behavioral Problems in Children With Autism Spectrum Disorder: Pilot Randomized Controlled Trial. JMIR Human Factors, 11(1), e52295. DOI: https://doi.org/10.2196/52295
25. Drakeford, O. M., & Majebi, N. L. (2024). Advancing Personalized Autism Interventions in the US A Data Analytics-Driven Conceptual Framework for Social Work. International Journal of Engineering Research and Development, 22(12), 385-391.
26. Haber, N., Voss, C., Daniels, J., Washington, P., Fazel, A., Kline, A., ... & Wall, D. P. (2020). A wearable social interaction aid for children with autism. arXiv preprint arXiv:2004.14281.
27. Das, B., Anderson, C., Villavicencio, T., Lantz, J., Foster, J., Hamlin, T., ... & Kwon, H. (2024). Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis. arXiv preprint arXiv:2407.21691.
28. Sideraki, A., & Anagnostopoulos, C. N. (2025). The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD. arXiv preprint arXiv:2505.02747.
29. Williams, R., & Yampolskiy, R. (2021). Understanding and avoiding ai failures: A practical guide. Philosophies, 6(3), 53. DOI: https://doi.org/10.3390/philosophies6030053
30. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. DOI: https://doi.org/10.1126/science.aax2342
31. Mittelstadt, B., Russell, C., & Wachter, S. (2019, January). Explaining explanations in AI. In Proceedings of the conference on fairness, accountability, and transparency (pp. 279-288). DOI: https://doi.org/10.1145/3287560.3287574
Downloads
Published
Data Availability Statement
https://docs.google.com/document/d/1tiiOx0Hzzg-NBqeVrbPhEiv7PyRPsckUrmnVsqnq2OA/edit?tab=t.0
Issue
Section
License
Copyright (c) 2025 Muna Abualhoul (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
