Artificial Intelligence in Bronchopulmonary Dysplasia: A Review of the Literature

Bronchopulmonary dysplasia (BPD) is a neonatal lung condition predominantly affecting preterm infants. Researchers have turned to computational tools, such as artificial intelligence (AI) and machine learning (ML), to better understand, diagnose, and manage BPD in patients. This study aims to provide a comprehensive summary of current AI applications in BPD risk stratification, treatment, and management and seeks to guide future research towards developing practical and effective computational tools in neonatal care. This review highlights breakthroughs in predictive modeling using clinical-, genetic-, biomarker-, and imaging-based markers. AI has helped advance BPD management strategies by optimizing treatment pathways and prognostic predictions through computational modeling. While these developments become increasingly clinically applicable, numerous challenges remain in data standardization, external validation, and the equitable integration of AI solutions into clinical practice. Addressing ethical considerations, such as data privacy and demographic representation, as well as other practical considerations will be essential to ensure the proper implementation of AI clinical tools. Future research should focus on prospective, multicenter studies, leveraging multimodal data integration to enhance early diagnosis, personalized interventions, and long-term outcomes for neonates at risk of BPD.