Artificial intelligence in predictive modeling for autism spectrum disorder diagnosis: a systematic review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: ABSTRACT Objective: This study aims to evaluate the performance of predictive models based on supervised learning techniques in supporting the diagnosis of Autism Spectrum Disorder (ASD), with emphasis on early detection, differential diagnosis, and identification of comorbidities. Method: We conducted a systematic review registered on the PROSPERO platform (ID: CRD42024522431) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (2020) criteria. We performed searches in the following databases: Web of Science, Scopus, PubMed/MEDLINE, Latin American and Caribbean Literature on Health Sciences (LILACS), and Scientific Electronic Library Online (SciELO). We included original articles published between 2018 and 2023. Results: A total of 364 studies were identified, of which 58 met the eligibility criteria, resulting in a final sample of 11607 individuals with ASD. Artificial intelligence (AI) demonstrated high potential for accurately identifying ASD and its variations, primarily using Machine Learning to analyze clinical, genetic, behavioral, and neuroimaging data. This promising approach enables early, differential diagnoses and identification of comorbidities, facilitating more targeted interventions. Conclusion: Supervised learning models show strong potential to support ASD diagnosis, enabling earlier and more accurate identification of clinical subtypes and comorbidities, and contributing to more individualized and effective care strategies.
Source: Jornal Brasileiro de Psiquiatria
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7127066565