Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods

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dc.contributor.authorTaneera, Sahar
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2026-04-09T13:38:11Z
dc.date.available2026-04-09T13:38:11Z
dc.date.issued2025
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAutism Spectrum Disorder (ASD) is a developmental disorder that affects one’s interpersonal skills, communication, and the desire to engage in repetitive activities. Early detection is important for treatment and management to be beneficial. This implies that the search for strategies for diagnosing ASD as fast and as successfully as possible is quite urgent which leads us to ask What is the fastest and most accurate way to diagnose ASD at an early age? An electronic search of various databases was done up to the end of December 2023. This consisted of the Quality Assessment Tool for Diagnostic Accuracy Studies—2 which was applied to assess the quality of the chosen studies. In this review, 45 papers were used. Even simple diagnostic procedures such as ADOS and ADI-R showed moderate reliability but were time-consuming and dependent on clinicians’ skills. Machine learning and deep learning techniques proved to have the potential to diagnose ASD with the help of many datasets, which can enhance the diagnostic precision and speed of the process. Conclusions: The application of AI techniques in identifying ASD has been stated as beneficial where there are few facilities for clinical examination. More investigations should be carried out to establish the real-life relevance of these approaches.
dc.identifier.citationTaneera, S. ve Alhajj, R. (2025). Diagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods. Network Modeling Analysis in Health Informatics and Bioinformatics, 14(1). http://dx.doi.org/10.1007/s13721-024-00499-6
dc.identifier.doi10.1007/s13721-024-00499-6
dc.identifier.issn2192-6662
dc.identifier.issn2192-6670
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85217537431
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1007/s13721-024-00499-6
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13409
dc.identifier.volume14
dc.identifier.wosWOS:001394373200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTaneera, Sahar
dc.institutionauthorAlhajj, Reda
dc.institutionauthorid0000-0001-6657-9738
dc.institutionauthorid0009-0002-1926-0868
dc.language.isoen
dc.relation.ispartofNetwork Modeling Analysis in Health Informatics and Bioinformatics
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectAdversarial Machine Learning
dc.subjectDeep Learning
dc.subjectDiagnosis
dc.subjectAutism Spectrum Disorder
dc.subjectASD
dc.subjectReview
dc.titleDiagnosis of autism spectrum disorder: a systematic review of clinical and artificial intelligence methods
dc.typeOther

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