Bridging the Gap in Health Literacy: Harnessing the Power of Large Language Models to Generate Plain Language Summaries from Biomedical Texts
Andrés Arias-Russi, Carolina Salazar-Lara, and Rubén Manrique
In Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health), May 2025
Health literacy enables individuals to navigate healthcare systems and make informed decisions. Plain language summaries (PLS) can bridge comprehension gaps by simplifying complex biomedical texts, yet their manual creation is both time-consuming and challenging. This study advances the field by (1) constructing a novel corpus of paired technical and plain language texts from medical trial libraries, (2) developing machine learning classifiers to rapidly identify plain language features, and (3) establishing a multi-dimensional evaluation framework that integrates computational metrics with human expertise. We iteratively optimized prompts for diverse large language models (LLMs)—including GPT models, Gemini 1.5, DeepSeek-R1, and Llama-3.2—to generate PLS variants aligned with domain-specific guidelines. Our classifier achieved 97.5% accuracy in distinguishing plain from technical language, and the generated summaries demonstrated high semantic equivalence to expert-written versions.