Retrieval-Augmented Generation in Healthcare Systems: A Systematic Review with Emphasis on Public Data
Abstract
Retrieval-Augmented Generation (RAG) enhances Large Language Models by integrating external knowledge retrieval. In public healthcare, this is relevant for handling heterogeneous data and specialized terminology. This paper presents a systematic review of RAG applications in healthcare, focusing on challenges and opportunities for Brazilian public data systems such as DataSUS. Following PRISMA, 145 studies were analyzed, with 14 meeting the inclusion criteria. Results indicate advances in hybrid retrieval and prompt engineering, but reveal gaps in regulatory compliance, multilingual support, and integration with Brazilian datasets. The study highlights research opportunities for developing RAG solutions tailored to public healthcare systems in Brazil.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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