Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review
IntroductionAutism 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 intelli...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1628216/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387650259091456 |
|---|---|
| author | Jun-Seok Sohn Eojin Lee Jae-Jin Kim Jae-Jin Kim Hyang-Kyeong Oh Eunjoo Kim Eunjoo Kim |
| author_facet | Jun-Seok Sohn Eojin Lee Jae-Jin Kim Jae-Jin Kim Hyang-Kyeong Oh Eunjoo Kim Eunjoo Kim |
| author_sort | Jun-Seok Sohn |
| collection | DOAJ |
| description | IntroductionAutism 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.MethodsWe 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.ResultsFrom 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.DiscussionThis 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 registrationhttps://osf.io/4gsyj/, identifier DOI: 10.17605/OSF.IO/4GSYJ. |
| format | Article |
| id | doaj-art-a5ccf09fb6b24397b133c258e2d78dbe |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-a5ccf09fb6b24397b133c258e2d78dbe2025-08-20T03:51:30ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-07-011610.3389/fpsyt.2025.16282161628216Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping reviewJun-Seok Sohn0Eojin Lee1Jae-Jin Kim2Jae-Jin Kim3Hyang-Kyeong Oh4Eunjoo Kim5Eunjoo Kim6Department of Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of Psychiatry, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of KoreaIntroductionAutism 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.MethodsWe 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.ResultsFrom 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.DiscussionThis 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 registrationhttps://osf.io/4gsyj/, identifier DOI: 10.17605/OSF.IO/4GSYJ.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1628216/fullautism spectrum disordergenerative artificial intelligencelarge language modelmachine learningdeep learningmental health |
| spellingShingle | Jun-Seok Sohn Eojin Lee Jae-Jin Kim Jae-Jin Kim Hyang-Kyeong Oh Eunjoo Kim Eunjoo Kim Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review Frontiers in Psychiatry autism spectrum disorder generative artificial intelligence large language model machine learning deep learning mental health |
| title | Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review |
| title_full | Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review |
| title_fullStr | Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review |
| title_full_unstemmed | Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review |
| title_short | Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review |
| title_sort | implementation of generative ai for the assessment and treatment of autism spectrum disorders a scoping review |
| topic | autism spectrum disorder generative artificial intelligence large language model machine learning deep learning mental health |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1628216/full |
| work_keys_str_mv | AT junseoksohn implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT eojinlee implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT jaejinkim implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT jaejinkim implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT hyangkyeongoh implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT eunjookim implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview AT eunjookim implementationofgenerativeaifortheassessmentandtreatmentofautismspectrumdisordersascopingreview |