Harnessing Large Language Models in Neonatal IVH: Exploring RAG Methodology for Prognostic Variable Discovery

This study demonstrates that LLMs can synthesize medical literature and extract clinically relevant prognostic variables for neonatal IVH outcomes. However, LLM outputs were susceptible to hallucinations and incomplete data synthesis, underscoring the need for rigorous clinical oversight and human validation to ensure reliability. The identified universal predictors provide a foundation for developing AI-assisted clinical decision support tools. Notable research gaps include the complete absence of resolution prediction studies and limited investigation of complication predictors, highlighting opportunities for future investigation in precision neonatology.