This paper presents exploratory results from the "Novo CorPop Saúde" project, evaluating whether validation by expert linguists improves readability levels compared to AI-generated simplifications. We analyzed original, AI-generated, and human-validated versions of thirty Patient Information leaflets. Using the Flesch Reading Ease Index, two adapted Dale-Chall metrics, and lexicometric analysis, we assessed automatic simplification against human strategies. Results indicate that while AI improves readability scores primarily through text compression, expert linguists prioritize elaborative strategies, maintaining key terminology while providing didactic explanations to fill semantic gaps. This suggests that human intervention plays a key role in balancing terminological precision with communicative accessibility, a nuance that purely reductive AI strategies might overlook. This study provided evidence on the use of the complex and simplified lexical repertoire of medicine leaflets for the "Novo CorPop Saúde".

While most essential medicines have become widely accessible across all social strata in Brazil due to government initiatives and market shifts, a significant barrier remains: the technical complexity of medication leaflets. This pragmatic and linguistic gap hinders patient comprehension of critical risks and benefits. Thus, adapting these texts into plain language patterns is crucial for patient safety and treatment adherence. Large language models have been increasingly effective as practical solutions for text simplification, an important Natural Language Processing (NLP) task that serves as a basis for several other linguistic and computational tasks. However, the scarcity of annotated datasets remains a bottleneck for rigorous evaluation. To bridge this gap, we propose a streamlined pipeline for generating simplified medical leaflets and introduce an initial benchmark dataset of 30 expertly annotated samples. Our results, supported by semantic and morphosyntactic evaluations, demonstrate that the proposed method produces high-quality, simplified content suitable for health applications.