TY - JOUR
AU - P. Solek
AU - E. Nurfitri
AU - I. Sahril
AU - T. Prasetya
AU - A. F. Rizqiamuti
AU - B. Burhan
AU - I. Rachmawati
AU - U. Gamayani
AU - K. Rusmil
AU - L. A. Chandra
AU - I. Afriandi
AU - K. Gunawan
A1 - 
AB - Objective: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. This systematic review examines the application of artificial intelligence (AI) in diagnosing ASD, focusing on pediatric populations aged 0-18 years. Materials and methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Inclusion criteria encompassed studies applying AI techniques for ASD diagnosis, primarily evaluated using metriclike accuracy. Non-English articles and studies not focusing on diagnostic applications were excluded. The literature search covered PubMed, ScienceDirect, CENTRAL, ProQuest, Web of Science, and Google Scholar up to November 9, 2024. Bias assessment was performed using the Joanna Briggs Institute checklist for critical appraisal. Results: The review included 25 studies. These studies explored AI-driven approaches that demonstrated high accuracy in classifying ASD using various data modalities, including visual (facial, home videos, eye-tracking), motor function, behavioral, microbiome, genetic, and neuroimaging data. Key findings highlight the efficacy of AI in analyzing complex datasets, identifying subtle ASD markers, and potentially enabling earlier intervention. The studies showed improved diagnostic accuracy, reduced assessment time, and enhanced predictive capabilities. Conclusion: The integration of AI technologies in ASD diagnosis presents a promising frontier for enhancing diagnostic accuracy, efficiency, and early detection. While these tools can increase accessibility to ASD screening in underserved areas, challenges related to data quality, privacy, ethics, and clinical integration remain. Future research should focus on applying diverse AI techniques to large populations for comparative analysis to develop more robust diagnostic models.
AD - Department of Child Health, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.; Department of Neurology, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.; Department of Pharmacology and Therapy, Gadjah Mada University Faculty of Medicine, Public Health and Nursing, Yogyakarta, Indonesia.; Department of Public Health, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.; Atma Jaya Catholic University of Indonesia Faculty of Medicine and Health Sciences, Jakarta, Indonesia.
AN - 40091547
BT - Turk Arch Pediatr
C5 - HIT & Telehealth; Healthcare Disparities
CP - 2
DA - Mar 3
DO - 10.5152/TurkArchPediatr.2025.24183
DP - NLM
IS - 2
JF - Turk Arch Pediatr
LA - eng
N2 - Objective: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. This systematic review examines the application of artificial intelligence (AI) in diagnosing ASD, focusing on pediatric populations aged 0-18 years. Materials and methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Inclusion criteria encompassed studies applying AI techniques for ASD diagnosis, primarily evaluated using metriclike accuracy. Non-English articles and studies not focusing on diagnostic applications were excluded. The literature search covered PubMed, ScienceDirect, CENTRAL, ProQuest, Web of Science, and Google Scholar up to November 9, 2024. Bias assessment was performed using the Joanna Briggs Institute checklist for critical appraisal. Results: The review included 25 studies. These studies explored AI-driven approaches that demonstrated high accuracy in classifying ASD using various data modalities, including visual (facial, home videos, eye-tracking), motor function, behavioral, microbiome, genetic, and neuroimaging data. Key findings highlight the efficacy of AI in analyzing complex datasets, identifying subtle ASD markers, and potentially enabling earlier intervention. The studies showed improved diagnostic accuracy, reduced assessment time, and enhanced predictive capabilities. Conclusion: The integration of AI technologies in ASD diagnosis presents a promising frontier for enhancing diagnostic accuracy, efficiency, and early detection. While these tools can increase accessibility to ASD screening in underserved areas, challenges related to data quality, privacy, ethics, and clinical integration remain. Future research should focus on applying diverse AI techniques to large populations for comparative analysis to develop more robust diagnostic models.
PY - 2025
SN - 2757-6256 (Print); 2757-6256
SP - 126
EP - 140+
ST - The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review
T1 - The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review
T2 - Turk Arch Pediatr
TI - The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review
U1 - HIT & Telehealth; Healthcare Disparities
U3 - 10.5152/TurkArchPediatr.2025.24183
VL - 60
VO - 2757-6256 (Print); 2757-6256
Y1 - 2025
ER -