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The Review of the Applications of AI Technology in the Detection and Treatment of Autism Spectrum Disorder (ASD)

Yucheng Xiong*, Guocheng Chen, Fan Zhang, Huan Sun

Abstract


Autism spectrum Disorder (ASD) not only has a significant impact on these patients themselves, but also has a serious impact on
their families and the society. This paper is a review about the application of AI technology in order to deal with ASD. It mainly from two
aspects: early detection and treatment of autism. AI-driven tools enhance early diagnosis by analyzing behavioral and developmental data,
facilitating timely interventions that improve developmental outcomes. Personalized treatment plans generated by AI algorithms cater to the
unique needs of each individual, maximizing therapeutic efficacy. Moreover, AI-powered social robotics and virtual reality simulations provide innovative methods for training social skills, allowing individuals with ASD to practice and develop communication abilities in controlled environments. Eventually, the appearance of LLM may accumulate the practical use of AI technology in dealing with ASD.

Keywords


ASD; AI technology; Machine learning; LLM; Robot

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References


[1] Li, B., Sharma, A., Meng, J., Purushwalkam, S. and Gowen, E., 2017.Applying machine learning to identify autistic adults using imitation: An exploratory study. PLOS ONE, 12(8), p.e0182652.

[2] Islam, Muhammad Nazrul & Omar, Kazi & Mondal, Prodipta & Khan, Nabila & Rizvi, Md. (2019). A Machine Learning Approach to

Predict Autism Spectrum Disorder. 10. 1109/ECACE. 2019. 8679454.

[3] Parikh, M., Li, H. and He, L., 2019. Enhancing Diagnosis of Autism with Optimized Machine Learning Models and Personal Characteristic Data. Frontiers in Computational Neuroscience, 13.

[4] Raj, S., & Masood, S. (2020, April 16). Analysis and detection of autism spectrum disorder using machine learning techniques. Procedia

Computer Science. Retrieved October 24, 2022, from https://www.sciencedirect.com/science/article/pii/S1877050920308656

[5] Y Fan, H Xiong, G Sun. Deep ASD Pred: A CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA

identifi cation[J]. (BMC Bioinformatics)

[6] Diehl, J. J., Schmitt, L. M., Villano, M., and Crowell, C. R. (2012). The clinical useof robots for individuals with autism spectrum disorders: a critical review. Res. Autism Spectrum Disorder. 6, 249262. doi: 10.1016/j.rasd.2011.05.006

[7] Scassellati, B., Admoni, H., and Mataric, M. (2012). Robots for use in autism research. Annu. Rev. Biomed. Eng. 14, 275294. doi:

10.1146/annurev-bioeng-071811-150036

[8] Robins, B., Dautenhahn, K., and Dickerson, P. (2009). From isolation to communication: a case study evaluation of robot assisted

play for children with autism with a minimally expressive humanoid robot, in 2009 Second International Conferences on Advances in

Computer-Human Interactions (IEEE), 205211.

[9] Bertacchini F, Demarco F, Scuro C, Pantano P and Bilotta E. (2023). A social robot connected with chatGPT to improve cognitive functioning in ASD subjects. Front. Psychol. 14:1232177. doi: 10.3389/fpsyg.2023.1232177

[10] Morgan, A. A., Abdi, J., Syed, M. A., Kohen, G. E., Barlow, P., and Vizcaychipi, M. P. (2022). Robots in healthcare: a scoping review.

Curr. Robot. Rep. 3, 271280. doi: 10.1007/s43154-022-00095-4

[11] Cho, Yujin et al. Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent

Psychological Counseling. ArXiv abs/2311.09243 2023.




DOI: http://dx.doi.org/10.70711/aitr.v2i4.4883

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