Pilot Study of Large Language Models as an Age-Appropriate Explanatory Tool for Chronic Pediatric Conditions

Cameron C Young, Ellie Enichen, Arya S Rao, Sidney Hilker, Alex Butler, Jessica Laird-Gion, Marc D. Succi
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Abstract

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.
大语言模型作为儿科慢性病适龄解释工具的试点研究
针对慢性病患儿的现有患者教育资源存在缺口。这项试点研究评估了大型语言模型(LLMs)向儿科患者提供适合其发展的慢性病解释的能力。两种常用的大型语言模型生成的回复能够准确、恰当、有效地传达复杂的医疗信息,使其成为在临床环境中增强患者理解和参与的潜在有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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