Utilizing Federated Health Databases and AI-Enhanced Neurodevelopmental Trajectory Mapping for Early Diagnosis of Autism Spectrum Disorder: A Review of Scalable Computational Models

Early diagnosis of Autism Spectrum Disorder (ASD) remains a critical challenge in pediatric neurodevelopmental care, with significant implications for intervention effectiveness and lifelong outcomes. Traditional diagnostic methods often rely on subjective behavioral assessments, limiting early detection, especially in resource-limited settings. This review explores the convergence of federated health databases and AI-driven neurodevelopmental trajectory mapping as scalable computational solutions for timely ASD diagnosis. Federated learning frameworks enable collaborative model training across decentralized data silos while preserving patient privacy, thereby overcoming the limitations of fragmented healthcare data ecosystems. Simultaneously, advanced machine learning techniques—including temporal graph networks, multimodal deep learning, and probabilistic modeling—facilitate individualized developmental path prediction. The paper systematically analyzes current architectures, datasets, and computational methods used to infer ASD risk from longitudinal health and behavioral data. It also discusses interpretability, scalability, and ethical considerations surrounding data governance and model transparency. The review concludes with recommendations for improving early ASD diagnosis through integrated AI-federated frameworks in global pediatric healthcare systems.