Association for Computational Linguistics 2025Arias Russi, AndresManrique, RubénSalazar Lara, Carolina2026-03-222026-03-22202510.48448/fjm7-d405https://doi.org/10.48448/fjm7-d405https://andeanlibrary.org/handle/123456789/86278Plain Language Summaries (PLS) play a critical role in improving health literacy, enabling informed decision-making and equitable healthcare access. However, writing PLS requires domain expertise and is time-consuming, making automation a valuable strategy for improving accessibility at scale. Automated methods often prioritize efficiency over comprehension, and the unique simplification requirements of medical documents challenge generic solutions. We present a multi-agent system for generating PLS, using Cochrane PLS as a proof of concept. The system decomposes simplification in four tasks, each handled by specialized agents: information extraction, writing, diagnostic, and evaluation. It integrates a medical glossary (20,637 terms) and a statistical analyzer that evaluates text patterns to guide revisions. We evaluated on 100 Cochrane abstracts using three models: Gemini-2.5-Pro, GPT-5 and the open model GPT-OSS-120B. The system achieved superior performance across semantic similarity, factual alignment, and readability metrics compared to single-prompt baselines. By combining AI agents with specific evaluation tools, this work offers a scalable solution that reduces the health literacy gap by making medical information more understandable to the public through accurate, readable summaries.ReadabilityPlain languageComputer scienceUnified Medical Language SystemPlain EnglishGlossaryAutomationDomain (mathematical analysis)Information retrievalData scienceother