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

Jun-Seok Sohn; Eojin Lee; Jae-Jin Kim; Hyang-Kyeong Oh; Eunjoo Kim

doi: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.

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

Authors: Jun-Seok Sohn ; Eojin Lee ; Jae-Jin Kim ; Hyang-Kyeong Oh ; Eunjoo Kim

Citation: FRONTIERS IN PSYCHIATRY, Vol.16 : 1628216, 2025-07

Journal Title: FRONTIERS IN PSYCHIATRY

Issue Date: 2025-07

MeSH: 0

Abstract: autism spectrum disorder; deep learning; generative artificial intelligence; large language model; machine learning; mental health; natural language processing; scoping review

Article Number: 10.3389/fpsyt.2025.1628216

DOI: 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.

Appears in Collections:: 1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers

Yonsei Authors: Kim, Eun Joo(김은주) https://orcid.org/0000-0003-3061-2051
Kim, Jae Jin(김재진) https://orcid.org/0000-0002-1395-4562

URI: https://ir.ymlib.yonsei.ac.kr/handle/22282913/207461

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