There exists a gap in existing patient education resources for children with chronic conditions. This pilot study assesses large language models' (LLMs) capacity to deliver developmentally appropriate explanations of chronic conditions to pediatric patients. Two commonly used LLMs generated responses that accurately, appropriately, and effectively communicate complex medical information, making them a potentially valuable tool for enhancing patient understanding and engagement in clinical settings.
Keywords: artificial intelligence; large language models; medical communication; pediatric chronic conditions.