Explainable AI in early autism detection: a literature review of interpretable machine learning approaches - Renuka Agrawal; Rucha Agrawal (Discover Mental Health)
Abstract Autism spectrum disorder (ASD) is a neurodevelopmental condition especially in children with a strong hereditary basis, making its early diagnosis challenging. Early detection of ASD enables individualized treatment programs that can improve social interactions, cognitive development, and communication abilities, hence lowering the long-term difficulties linked to the disorder. Early detection helps in therapeutic interventions, which can help children acquire critical skills and lessen the intensity of symptoms. Despite their remarkable predictive power, machine learning models are frequently less accepted in crucial industries like healthcare because of their opaque character, which makes it challenging for practitioners to comprehend the decision-making process. Explainable AI (XAI), an extension to AI, has emerged due to issues like trust, accountability, and transparency caused by the opaque nature of AI models, especially deep learning. XAI aims to make AI’s decision-making processes easier to understand and more reliable. The present study delves into the extensive applications of XAI in diverse fields including healthcare, emphasizing its significance in guaranteeing an ethical and dependable implementation of AI. The article goes into additional detail in a specialized assessment of AI and XAI applications in research on ASD, showing how XAI can offer vital insights into identifying, diagnosing, and treating autism.