A Multi-Agent Framework with Diagnostic Feedback for Iterative Plain Language Summary Generation from Cochrane Medical Abstracts

Abstract

Plain 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.