Decoding large language models for radiology: strategies for fine-tuning and prompt engineering

Area of Science:

• Medical Imaging Informatics
• Artificial Intelligence in Healthcare
• Radiology Workflow Optimization

Background:

• Large Language Models (LLMs) offer advanced capabilities for automating complex tasks in radiology.
• Applications include report generation, summarization, and data collection for research.
• Challenges include domain-specific adaptation and the potential for factual inaccuracies affecting patient care.

Purpose of the Study:

• To review recent advancements in applying LLMs to radiology.
• To explore fine-tuning and prompt optimization techniques for LLM implementation.
• To provide a framework for integrating LLMs into clinical radiology settings.

Main Methods:

• Review of fine-tuning strategies for LLMs in medical contexts.
• Analysis of prompt optimization techniques to enhance LLM accuracy.
• Evaluation of advantages and limitations of current LLM adaptation methods.

Main Results:

• Fine-tuning and prompt optimization are effective in mitigating LLM errors.
• These strategies improve the factual accuracy and reliability of LLMs for clinical use.
• Understanding these principles is crucial for maintaining LLM performance.

Conclusions:

• LLMs hold significant potential to transform radiology workflows.
• Careful adaptation using fine-tuning and prompt engineering is essential for safe and effective deployment.
• This review offers insights for practical LLM integration in radiology.