JMIR Publications

ABSTRACT

Retrieval-Augmented Generation models have emerged as a powerful technique for optimizing general large language models in specialized domains, and are being increasingly adopted by researchers in the medical field. This article acknowledges the significant potential of RAG to enhance clinical decision-making. However, it argues that researchers and practitioners must proactively address the ethical risks associated with RAG implementation in healthcare. Key considerations include ensuring accuracy, fairness, transparency, and accountability, as well as maintaining essential human oversight, as discussed in detail. We propose that robust data governance, explainable AI techniques, and continuous monitoring are critical components of a responsible RAG implementation strategy. Ultimately, realizing the benefits of RAG while mitigating ethical concerns requires collaboration among healthcare professionals, AI developers, and policymakers, fostering a future where AI supports patient safety, reduces disparities, and improves the quality of nursing care.