Accelerating autism spectrum disorder care: a rapid review of data science applications in diagnosis and intervention

Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis and intervention landscape for Autism Spectrum Disorder (ASD). This rapid review examines these approaches' current applications, benefits, limitations, and ethical considerations while identifying key research gaps and future directions. Data-driven methodologies offer significant advantages, such as enhanced diagnostic accuracy, personalized interventions, and increased accessibility, particularly in resource-limited settings. However, challenges like data quality, algorithmic bias, and interpretability hinder widespread implementation. Additionally, ethical concerns regarding privacy, consent, and equity necessitate careful navigation. Despite these advancements, substantial research gaps remain, including the lack of diverse datasets, limited longitudinal studies, and insufficient generalizability across populations. Future studies must prioritize addressing these gaps by fostering collaboration, ensuring ethical transparency, and developing inclusive, scalable solutions to improve patient outcomes. This review underscores the transformative potential of data science in accelerating ASD care while emphasizing the need for continued innovation and responsible application.