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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented

Anna Fink^1,2, Alexander Rau³, Elmar Kotter⁴

¹Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät der Universität Freiburg, Freiburg, Deutschland. anna.fink@uniklinik-freiburg.de.

Radiologie (Heidelberg, Germany)

|February 21, 2025

Summary

Radiologists can optimize large language models (LLMs) using prompt engineering, zero-shot learning, and retrieval-augmented generation (RAG). These techniques enhance LLM accuracy and relevance for improved patient care in radiology.

Keywords:

Artificial intelligence Deep learning Machine learning Natural language processing Neural Networks, Computer

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Area of Science:

Artificial Intelligence in Medical Imaging
Natural Language Processing Applications
Radiology Workflow Optimization

Context:

Increasing volume of radiological examinations necessitates advanced tools.
Large language models (LLMs) show potential for supporting radiologists.
Effective interaction with LLMs is crucial for reliable results.

Purpose:

To provide an overview of LLM interaction techniques for radiology.
To detail prompt engineering, zero-shot learning, and retrieval-augmented generation (RAG).
To offer practical guidance for applying these techniques in radiological practice.

Summary:

Advanced techniques like prompt engineering, zero-shot, and few-shot learning enable task-specific LLM adaptation without retraining.
Retrieval-augmented generation (RAG) integrates current, domain-specific information, boosting LLM accuracy and relevance.
These methods allow efficient integration of LLMs into routine radiology workflows.

Impact:

Optimized LLM interaction enhances diagnostic accuracy and efficiency.
Targeted strategies empower radiologists to leverage AI tools effectively.
Improved LLM application in radiology can lead to better patient care outcomes.