Retrieval augmented generation for large language models in healthcare: A systematic review
Lameck Mbangula Amugongo, Pietro Mascheroni, Steven E. Brooks et al.
2025 · PLOS Digital Health · 119 citations
Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and…
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