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Review
. 2025 Apr 21:11:20552076251337177.
doi: 10.1177/20552076251337177. eCollection 2025 Jan-Dec.

Enhancing medical AI with retrieval-augmented generation: A mini narrative review

Omid Kohandel Gargari  1 , Gholamreza Habibi  1
Affiliations
Review

Enhancing medical AI with retrieval-augmented generation: A mini narrative review

Omid Kohandel Gargari et al. Digit Health. .

Abstract

Retrieval-augmented generation (RAG) is a powerful technique in artificial intelligence (AI) and machine learning that enhances the capabilities of large language models (LLMs) by integrating external data sources, allowing for more accurate, contextually relevant responses. In medical applications, RAG has the potential to improve diagnostic accuracy, clinical decision support, and patient care. This narrative review explores the application of RAG across various medical domains, including guideline interpretation, diagnostic assistance, clinical trial eligibility screening, clinical information retrieval, and information extraction from scientific literature. Studies highlight the benefits of RAG in providing accurate, up-to-date information, improving clinical outcomes, and streamlining processes. Notable applications include GPT-4 models enhanced with RAG to interpret hepatologic guidelines, assist in differential diagnosis, and aid in clinical trial screening. Furthermore, RAG-based systems have demonstrated superior performance over traditional methods in tasks such as patient diagnosis, clinical decision-making, and medical information extraction. Despite its advantages, challenges remain, particularly in model evaluation, cost-efficiency, and reducing AI hallucinations. This review emphasizes the potential of RAG in advancing medical AI applications and advocates for further optimization of retrieval mechanisms, embedding models, and collaboration between AI researchers and healthcare professionals to maximize RAG's impact on medical practice.

Keywords: Retrieval-augmented generation (RAG); artificial intelligence; clinical decision support; clinical trial eligibility screening; diagnostic assistance; guideline interpretation; large language models (LLMs); medical applications.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Use of retrieval augmented generation in medicine.
Figure 2.
Figure 2.
Challenges of using retrieval augmented generation in medicine.
See this image and copyright information in PMC

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