Dealing with Autism Spectrum Disorders: Journey from Traditional Methods to Artificial Intelligence

Background World Health Organisation (WHO) in 2024 identified that approximately one in 100 children globally has autism spectrum disorder (ASD). ASD is a collection of neurodevelopmental disorders that impact a person’s ability to socially interact and communicate, which can typically be noticed in early childhood. While ‘autism’ as a term was initially used for schizophrenic patients, later psychiatrists Dr. Kanner and paediatrician Dr. Asperger introduced it as a syndrome in children with behavioural differences in social interaction and communication with restrictive and repetitive interests. In today’s time, the umbrella term ‘ASDs’ is used to describe a clinically heterogeneous group of neurodevelopmental disorders (NDDs). Purpose To examine the role of traditional approaches and the potential effectiveness of artificial intelligence (AI) methods in dealing with ASDs for improving the accuracy in its diagnosis and treatment. Methodology The study adopts a narrative review approach to understand the application of AI in ASD. For this purpose, around a hundred research articles were selected from the years 2010–2024. Inclusion and exclusion criteria were identified. The review is organised and grounded on the medical treatment, occupational remedy, vocational remedy, psychology, family remedy and recuperation engineering. Results and Conclusion The results show the undisputed role of AI and its ability to identify early indicators of autism, in accordance with the UN Sustainable Development Goal 3 (Good Health and Well-being) and Goal 16 (Peace, Justice and Strong Institutions). Further, healthcare sectors which are using a variety of AI analyses on data sources, genetics, neuroimaging, behavioural patterns and electronic medical records are able to early detect for individualised evaluation of ASD. The significance of timely interventions with the help of machine learning (ML) algorithms demonstrates high accuracy in differentiating ASD from neurotypical development and other developmental disorders. AI-driven therapeutic interventions expand social interactions and communication skills in people with ASD in the form of virtual reality-based training, augmentative communication systems and robot-assisted therapies. Thus, the future of AI in ASD holds promise for improving diagnostic accuracy, implementing telehealth platforms and customising treatment plans, despite obstacles such as data privacy and interpretability.