Unveiling AI-Enhanced Strategies for Early Detection and Support in Neurodevelopmental Disorders Diagnosis: A Systematic Review

Abstract

Neurodevelopmental Disorders (NDDs) pose a significant challenge to the societal and cognitive development of children, toddlers, adolescents, and adults. Common NDDs include Dysarthria, Dyscalculia, Dysgraphia, Dyslexia, Attention Deficit Hyperactivity Disorder, and Autism Spectrum Disorder. Thus, early diagnosis and support on individuals developing NDDs might help them to overcome mental challenges and mitigate the potential development of negative emotions. In this article, a Systematic Literature Review (SLR) of AI-enhanced strategies for Early Detection and Support in Diagnosing NDDs is performed to select and analyze 23 studies published between July 2019 and July 2024, which were critically analyzed to evaluate the performance of AI algorithms, the characteristics of NDD datasets, and trends in global research on NDDs. Additionally, the review highlights the common challenges faced by individuals with NDDs, such as difficulties in reading, writing, speaking, mathematical reasoning, and information processing, which often lead to frustration, low self-esteem, and disengagement from academic and social activities which lead to opening several future research studies. Thus, as future studies can develop deeper into the development of AI-integrated applications for screening and intervention processes for multiple NDDs among multiple age categories in detecting the level of NDDs, application of enhanced feature engineering in identifying and incorporating more informative features in detection of the NDDs along with integrating explainability and interpretability by developing models with enhanced explainable AI techniques. To conclude, this study provides a foundation for advancing AI-driven strategies in the early diagnosis and support of individuals with NDDs, intending to foster better outcomes and improved quality of life.

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