A Review of Developments in Generative AI, Machine Learning, and Neuroimaging for the Diagnosis of Autism

• Published in: VFAST Transactions on Software Engineering Vol. 13; no. 2; pp. 68 - 82
• Main Authors: Ali, Sumera, Baloch, Zartasha, Narejo, Sanam, Shaikh, Ahmed Muhammad
• Format: Journal Article
• Language: English
• Published: 12.05.2025
• ISSN: 2411-6246; 2309-3978
• DOI: 10.21015/vtse.v13i2.2089

 Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with be- havioral and neurological variations. Diagnosing ASD is complicated because behavioral assessments vary from person to person, and it is challenging as it relies on subjective behavioral assessments rather than objective medical tests. Following the PRISMA guide- lines for a systematic review, 53 papers from ScienceDirect, IEEE Xplore, and PubMed databases published between 2015 and 2024 have been selected, to determine how ML (Machine Learning) and GANs (Generative Adversarial Network) enhance the use of neuroimaging techniques for the early detection of ASD. The study addresses significant dataset issues by combining discriminative machine learning models, such as SVMs (Sup- port Vector Machine) and CNNs (Convolutional Neural Network), for pattern recognition in sMRI (Structural Magnetic Resonance Imaging), fMRI (Functional Magnetic Resonance Imaging), and EEG (Electroencephalography) with GANs for data augmentation and en- hanced feature learning. The review highlights how AI algorithms can be helpful in the diagnosis of ASD, particularly in areas with limited medical facilities. For example, due to limited resources, just 1 in 628 children in Pakistan are diagnosed with autism. Future re- search will focus on methods that combine explainable AI, generative and discriminative techniques, and structured datasets, while improving model transparency, generalizabil- ity, and avoiding biased data. By integrating AI with clinical information, this research helps to design treatment protocols that may be customized to each patient’s specific requirements and enables earlier identification of autism.