Advances in Autism: a bibliometric analysis

This paper provides a comprehensive overview of AI treatment research for Autism Spectrum Disorder (ASD) from 2007 to 2023, focusing on global contributions across countries, institutions, authors, and keywords. The United States leads with 164 documents and 4988 citations, highlighting its central role in advancing AI technologies for ASD therapies, followed by significant contributions from China (90 documents, 1190 citations) and India (65 documents, 564 citations). Institutions like Stanford University and McGill University demonstrate substantial research output, while authors such as Dennis Wall are prominent with contributions that make diagnosing Autism much more efficient with the use of AI. Keywords like “Machine learning”, “Autism spectrum disorder”, and “Children” dominate, reflecting ongoing efforts to leverage technology for ASD interventions. Overall, this analysis underscores a dynamic global effort to enhance ASD treatment methodologies through collaborative research and technological innovations.
1. Mertz L. AI, Virtual Reality, and Robots Advancing Autism Diagnosis and Therapy. IEEE Pulse.2021;12(5):6-10.doi:10.1109/mpuls.2021.3113092
2. Shushma G, Jacob IJ. Autism Spectrum Disorder detection using AI Algorithm. In: 2022 Second International Conference on Artificial Intelligence and Smart Energy(ICAIS).;2022. doi:10.1109/icais53314.2022.9743011
3. Helmy E,Elnakib A, ElNakieb Y,et al. Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey. Biomedicines.2023;11(7):1858.doi:10.3390/biomedicines11071858
4. Mertz L. AI, Virtual Reality, and Robots Advancing Autism Diagnosis and Therapy. IEEE Pulse.2021;12(5):6-10.doi:10.1109/mpuls.2021.3113092
5. Song JW,Yoon NR, Jang SM, Lee GY, Kim BN. Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Journal of Korean Academy of Child and Adolescent Psychiatry.2020;31(3):97-104.doi:10.5765/jkacap.200021
6. MacFarlane H, Salem AC, Chen L, Asgari M,Fombonne E. Combining voice and language features improves automated autism detection. Autism Research.2022;15(7):1288-1300.doi:10.1002/aur.2733
7. Yang X, T P, Zhang N. A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification. International Journal of Advanced Computer Science and Applications.2020;11(4).doi:10.14569/ijacsa.2020.0110401
8. Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders. IEEE Journals & Magazine| IEEE Xplore. Published online 2019.https://ieeexplore.ieee.org/document/8895818/
9. Shahamiri SR, Thabtah F. Autism AI: a New Autism Screening System Based on Artificial Intelligence. Cognitive Computation.2020;12(4):766-777.doi:10.1007/s12559-020-09743-3
10. Data and Statistics on Autism Spectrum Disorder. Autism Spectrum Disorder(ASD). May 16,2024.https://www.cdc.gov/autism/data-research/index.html
11. Abbas H, Garberson F, Liu-Mayo S, Glover E, Wall DP. Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children. Scientific Reports.2020;10(1).doi:10.1038/s41598-020-61213-w