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arXiv AI By Rafael Mu\~noz-Terol, Jes\'us Peral, Sandra Amador, David Gil

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

Read the original on arXiv AI →

The article reviews 55 machine‑learning studies on autism spectrum disorder (ASD) published between 2017 and 2023, focusing on how ML can aid early diagnosis and treatment. It finds that supervised learning dominates current research, while deep learning is gaining traction as data volumes grow. The review highlights the need for models that fuse complex data—such as genetic, clinical, wearable, and biometric sources—to improve diagnostic accuracy and enable continuous, non‑intrusive monitoring.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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