A systematic bibliometric and meta analysis of key factors and emerging AI and ML insights in shaping child cognitive...

The study aims to provide scholars, professionals and others with a thorough analysis of how advanced technologies, specifically Artificial Intelligence (AI) and Machine Learning (ML), can be integrated in the early diagnosis of children's cognitive development. Adopting both systematic and bibliometric approaches, the review encompasses 122 journal articles published over the last 10 years. The analysis reveals that the majority of research work for the diagnosis of cognitive development in early childhood has been done via traditional statistical methods. The application of integrating AI and ML in early cognitive diagnosis remains limited and underexplored. The study provides academics and practitioners with important insights for continuing endeavors and possible future advances by identifying the primary factors, focus, and trends in child cognitive development. This will promote a deeper understanding of approaches to diagnosing children's cognitive development. This understanding is especially relevant in low-resource settings like India, where accessible and non-stigmatizing cognitive assessment tools can empower parents to recognize developmental delays early. Integrating AI and ML-driven solutions with culturally adapted, user-friendly platforms can bridge existing gaps and support timely interventions for long-term cognitive growth.