Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication, restricted interests, and repetitive behaviors. Early diagnosis and intervention are critical for improving long-term outcomes for individuals with ASD. In recent years, artificial intelligence (AI) and machine learning (ML) techniques have emerged as powerful tools for assisting in the early detection and diagnosis of ASD. This review provides a comprehensive analysis of the current state of AI-driven approaches for ASD early diagnosis, examining applications across various data modalities including neuroimaging, eye-tracking, electroencephalography (EEG), clinical questionnaires, and genomic data. We systematically review recent advances in deep learning, traditional machine learning, and multimodal fusion techniques, evaluating their diagnostic performance and clinical applicability. Furthermore, we discuss current challenges including data heterogeneity, model interpretability, algorithmic fairness, and the need for large-scale validation studies. Finally, we outline future research directions and potential pathways for translating AI-based diagnostic tools into clinical practice. Our findings suggest that while significant progress has been made, continued interdisciplinary collaboration between computer scientists, clinicians, and researchers is essential for developing robust, interpretable, and clinically deployable AI systems for the early diagnosis of ASD.

Cong Luo, Yaqi Xu. AI-Driven Early Diagnosis of Autism Spectrum Disorder: Current Status and Future Perspectives. Academic Journal of Medicine & Health Sciences (2026), Vol. 7, Issue 2: 1-7. https://doi.org/10.25236/AJMHS.2026.070201.

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