RAG in Health Care: A Novel Framework for Improving Communication and Decision-Making by Addressing LLM Limitations

Within the field of artificial intelligence (AI), large language models (LLMs) have the potential to transform the delivery of medical information. LLMs, as a subset of generative AI, have demonstrated value in content creation, idea generation, and interactive communication. However, their inherent limitations, such as the need for up-to-date information, hallucinations of incorrect facts, and a reliance on public-domain data, restrict the full potential of generative AI within the health care setting. To address these limitations, retrieval-augmented generation (RAG) offers a novel framework by connecting LLMs with external knowledge, enabling them to access information beyond their training data. Within the health care domain, additional datasets could include peer-reviewed studies, gated medical compendiums, and the internal policies of health care organizations such as hospitals or pharmaceutical companies. By leveraging RAG, existing generative AI tools gain the capability to consider both public and private information, expanding their application and enhancing accuracy and relevance within the health care setting. The utility of RAG in the health care setting has yet to be fully explored, but it has the potential to revolutionize the industry. This article seeks to outline present and future use cases of RAG for health care information exchange within both clinical and industrial settings.