Advanced Prompt Engineering for Large Language Models in Interventional Radiology: Practical Strategies and Future...

Area of Science:

• Medical Imaging and Interventional Radiology
• Artificial Intelligence in Medicine
• Clinical Workflow Optimization

Background:

• Large language models (LLMs) are increasingly adopted in clinical settings.
• Advanced prompting techniques are crucial for optimizing LLM performance in specialized fields like interventional radiology (IR).

Purpose of the Study:

• To present a structured guide on five advanced prompting strategies for LLMs in IR.
• To demonstrate practical IR-specific use cases for each prompting approach.
• To outline requirements, considerations, and limitations for LLM integration in IR.

Main Methods:

• Chain-of-verification
• Chain-of-density
• Reasoning and acting
• Generated knowledge prompting
• Retrieval-augmented generation

Main Results:

• Demonstrated how prompts can guide LLMs to generate transparent, patient-tailored, and evidence-grounded responses.
• Outlined technical requirements, clinical considerations, and limitations, including adversarial prompting risks.
• Explored emerging directions like agentic workflows and the need for IR-specific benchmarks.

Conclusions:

• Advanced prompting provides a foundation for safe and effective LLM integration in high-stakes IR workflows.
• Emphasizes the need for human-in-the-loop design, radiology-specific benchmarks, and regulatory standards.
• Offers value to clinicians, researchers, and developers involved in AI in medicine.