A Systematic Literature Review on the Application of Explainable AI Approaches in the Assessment of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with significant heterogeneity in symptoms and comorbid conditions. Studies have shown the value of early and accurate diagnosis in mitigating the associated social and behavioral challenges. Recently, studies have demonstrated the potential of machine learning (ML) models in identifying ASD patterns from neuroimaging, genetic, and behavioral data. However, the limited interpretability of these models poses a barrier to their clinical adoption. Explainable Artificial Intelligence (XAI) offers a solution that allows clinicians to understand how diagnostic decisions are made. Based on a systematic literature review, the present study provides a thematic synthesis of 34 relevant studies that could assist in the creation of proper clinical instruments and identifies key challenges and the need for future studies to propose clinician-centered explainability metrics.