Retrieval-Augmented Generation with Large Language Models in Radiology: From Theory to Practice

Area of Science:

• Artificial Intelligence in Radiology
• Deep Learning Applications
• Natural Language Processing in Medical Imaging

Background:

• Large Language Models (LLMs) show potential for radiology workload but suffer from hallucinations and opaque sourcing.
• Retrieval-Augmented Generation (RAG) offers a solution by integrating verifiable information into LLM responses.
• Current RAG models require refinement for handling extensive data and complex dialogues.

Purpose of the Study:

• To provide an overview of recent advancements in RAG-based LLMs for radiology.
• To identify future research directions in this rapidly evolving field.
• To demonstrate practical applications of these AI techniques in radiology practice.

Main Methods:

• Overview of LLM architectures including few-shot and zero-shot learning.
• Integration strategies for Retrieval-Augmented Generation (RAG).
• Exploration of multistep reasoning and agentic RAG capabilities.

Main Results:

• RAG-based LLMs can streamline radiology workflows with reliable, verifiable information.
• Advancements in LLM architecture support complex multiagent dialogues and large datasets.
• Exemplary cases illustrate the utility of these AI techniques in clinical settings.

Conclusions:

• RAG-based LLMs represent a significant step towards overcoming LLM limitations in radiology.
• Continued research and development are crucial for optimizing RAG models in medical imaging.
• These AI advancements promise to enhance efficiency and accuracy in radiological practice.