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Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review

Authors
 Sohn, Jun-Seok  ;  Lee, Eojin  ;  Kim, Jae-Jin  ;  Oh, Hyang-Kyeong  ;  Kim, Eunjoo 
Citation
 FRONTIERS IN PSYCHIATRY, Vol.16, 2025-07 
Journal Title
FRONTIERS IN PSYCHIATRY
ISSN
 1664-0640 
Issue Date
2025-07
Keywords
autism spectrum disorder ; generative artificial intelligence ; large language model ; machine learning ; deep learning ; mental health ; natural language processing ; scoping review
Abstract
Introduction Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and restrictive, repetitive behaviors. Current diagnostic and intervention pathways rely heavily on clinician expertise, leading to delays and limited scalability. Generative artificial intelligence (GenAI) offers emerging opportunities for automatically assisting and personalizing ASD care, though technical and ethical concerns persist.Methods We conducted systematic searches in Embase, PsycINFO, PubMed, Scopus, and Web of Science (January 2014 to February 2025). Two reviewers independently screened and extracted eligible studies reporting empirical applications of GenAI in ASD screening, diagnosis, or intervention. Data were charted across GenAI architectures, application domains, evaluation metrics, and validation strategies. Comparative performance against baseline methods was synthesized where available.Results From 553 records, 10 studies met the inclusion criteria across three domains: (1) screening and diagnosis (e.g., transformer-based classifiers and GAN-based data augmentation), (2) assessment and intervention, (e.g., multimodal emotion recognition and feedback systems), and (3) caregiver education and support (e.g., LLM-based chatbots). While most studies reported potential performance improvements, they also highlighted limitations such as small sample sizes, data biases, limited validation, and model hallucinations. Comparative analyses were sparse and lacked standardized metrics.Discussion This review (i) maps GenAI applications in ASD care, (ii) compares GenAI and traditional approaches, (iii) highlights methodological and ethical challenges, and (iv) proposes future research directions. Our findings underscore GenAI's emerging potential in autism care and the prerequisites for its ethical, transparent, and clinically validated implementation.Systematic review registration https://osf.io/4gsyj/, identifier DOI: 10.17605/OSF.IO/4GSYJ.
Files in This Item:
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DOI
10.3389/fpsyt.2025.1628216
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun Joo(김은주) ORCID logo https://orcid.org/0000-0003-3061-2051
Kim, Jae Jin(김재진) ORCID logo https://orcid.org/0000-0002-1395-4562
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/207369
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