A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

A systematic review of 55 studies from 2017 to 2023 finds that supervised learning dominates machine learning applications for autism spectrum disorder (ASD) diagnosis and treatment, with deep learning expanding as data availability grows. The review, published on arXiv (2608.18188v1), highlights the need for models integrating genetic and clinical data and the potential of wearable devices for continuous monitoring, emphasizing interdisciplinary collaboration and expanded datasets.

arXiv:2608.18188v1 Announce Type: new Abstract: Autism spectrum disorder ASD is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning ML techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.