AI-powered decision support system for early diagnosis and risk assessment in neonatal and pediatric care

Early diagnosis and accurate risk assessment are critical determinants of survival and long-term health outcomes in neonatal and pediatric care. However, clinical decision-making is often challenged by limited time, high patient variability, and incomplete data. This study presents the development of an AI-powered decision support system (DSS) designed to assist clinicians in identifying early warning signs of critical conditions in neonates and children.

Our approach integrates multimodal clinical data including electronic health records, vital signs, laboratory values, and imaging data into a unified framework. We employed machine learning and deep learning models such as gradient boosting, recurrent neural networks (RNNs), and transformer-based architectures to predict disease onset, stratify risk, and recommend personalized interventions. Special emphasis was placed on conditions with high mortality and morbidity burdens, including neonatal sepsis, congenital heart disease, respiratory distress syndrome, and pediatric infections.

The DSS was trained and validated on a multi-center dataset comprising over 25,000 neonatal and pediatric patient records. Results demonstrate that our AI models achieved superior performance compared to existing risk-scoring systems, with an average AUC > 0.92 for early sepsis detection and a 20–25% improvement in predictive accuracy for adverse event forecasting. Importantly, the system provides interpretable outputs, including feature-attribution heatmaps and clinical reasoning trails, ensuring transparency and physician trust.

Beyond predictive accuracy, the DSS incorporates an adaptive risk dashboard that enables real-time monitoring in neonatal intensive care units (NICUs). The platform is designed for scalability across diverse healthcare settings, from tertiary hospitals to resource-constrained clinics, with lightweight deployment options via cloud and edge computing.

Our findings highlight the transformative potential of AI in augmenting clinical decision-making and reducing preventable mortality in neonatal and pediatric populations. Future work will involve prospective validation in clinical trials, integration with bedside monitoring devices, and the inclusion of genomic and metabolomic biomarkers to further refine predictive capabilities.

In conclusion, this research underscores the role of AI-powered decision support as a cornerstone of next-generation neonatal and pediatric care, bridging the gap between data-driven precision medicine and real-world clinical practice.

Amaan Arif is a researcher specializing in bioinformatics, artificial intelligence, and clinical data science with a focus on healthcare applications. He has worked on projects involving genome analysis, disease biomarker prediction, and AI-driven diagnostic tools. Amaan is currently engaged in developing innovative computational frameworks for integrating clinical and genomic data to improve patient outcomes. His research interests lie in the intersection of AI, pediatrics, and precision medicine, and he aspires to advance next-generation healthcare technologies through interdisciplinary collaboration and translational research