Harnessing Large Language Models in Neonatal Intraventricular Hemorrhage: Exploring Retrieval Augmented Generation
1Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, United States.
American Journal of Perinatology
|March 23, 2026
Summary
Large language models can extract prognostic factors for neonatal intraventricular hemorrhage (IVH) from medical literature. Human validation is crucial due to potential LLM inaccuracies in predicting IVH outcomes.
Area of Science:
- Neonatal Medicine
- Artificial Intelligence
- Medical Informatics
Background:
- Neonatal intraventricular hemorrhage (IVH) is a significant concern in preterm infants, with numerous studies investigating its prognostic variables.
- Accurate prediction of IVH outcomes is essential for timely clinical intervention and improved patient management.
- Existing literature synthesis methods can be time-consuming and may not capture all relevant predictive factors.
Purpose of the Study:
- To assess the capability of large language models (LLMs) to autonomously synthesize literature and extract prognostic variables for neonatal IVH.
- To evaluate LLMs' performance in ranking clinical features and stratifying risk for IVH outcomes.
- To explore the potential of AI in identifying key predictors for mortality, progression, complications, and resolution of IVH.
Main Methods:
- A systematic literature review combined with retrieval augmented generation (RAG) methodology was employed.
- GPT-4 and Claude Sonnet LLMs were utilized to identify and extract data from peer-reviewed studies on IVH prediction in preterm neonates.
- Data extraction followed TRIPOD AI guidelines, with semi-automated RAG extraction and manual validation to ensure accuracy and mitigate hallucinations.
Main Results:
- LLMs identified 28 relevant studies, extracting 14 distinct prognostic predictors across four outcome domains.
- Key predictors identified include gestational age (41%), birth weight (25%), and APGAR scores (34%).
- A preliminary risk stratification model indicated high-risk neonates (<28 weeks, <1000g, APGAR <3) with >70% progression risk and >50% mortality risk.
Conclusions:
- LLMs can effectively synthesize medical literature and extract clinically relevant prognostic variables for neonatal IVH.
- Rigorous clinical oversight and human validation are necessary to address LLM-related hallucinations and ensure data reliability.
- Identified predictors form a basis for AI-assisted clinical decision support tools, with future research needed for complication and resolution prediction.
