A survey on retrieval-augmentation generation (RAG) models for healthcare applications

Abstract

Retrieval-augmented generation (RAG) models have become crucial in healthcare applications, significantly enhancing the relevance and reliability of AI-driven insights by combining the generative capabilities of large language models (LLMs) with retrieval-based methods. As healthcare data demand precision and accountability, RAG models address critical limitations of LLMs, such as the tendency to “hallucinate” or produce inaccurate information—by incorporating real-time retrieval from trusted medical knowledge bases and clinical literature. This dual process of retrieving and generating ensures that responses are both contextually accurate and aligned with the latest clinical evidence, making RAG models especially valuable for medical question answering, diagnostics, and treatment planning. This paper presents an in-depth exploration of RAG models and LLMs, specifically within healthcare contexts, to meet the unique demands of medical data processing and decision support. It reviews various RAG architectures, including Naive, Advanced, and Modular RAG approaches, discussing how each framework optimizes retrieval depth, response quality, and computational efficiency. By reducing errors in patient-care recommendations, RAG models play an essential role in scenarios that require high precision and accountability. Additionally, this survey addresses the ethical considerations and transparency requirements for deploying RAG models in healthcare, identifying current challenges and future directions, such as enhancing source interpretability and adapting RAG frameworks for specialized medical fields. By systematically analyzing RAG techniques, this paper provides a comprehensive guide to the state-of-the-art in RAG applications within healthcare, positioning RAG models as a transformative tool for advancing AI-supported healthcare outcomes.