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

Abstract

As large language models (LLMs) become increasingly integrated into clinical workflows, advanced prompting strategies offer new opportunities and challenges for their application in interventional radiology (IR). This Clinical Perspective presents a structured guide to five advanced prompting approaches: chain-of-verification, chain-of-density, reasoning and acting, generated knowledge prompting, and retrieval-augmented generation. Each approach is illustrated with practical IR-specific use cases that demonstrate how prompts can guide LLMs to produce transparent, patient-tailored, and evidence-grounded responses. We also outline technical requirements for implementation, clinical considerations for combining strategies, and key limitations, including the risk of adversarial prompting whereby manipulative inputs may bypass guardrails or distort outputs. Finally, we explore emerging directions using agentic workflows and emphasize the need for radiology-specific benchmarks, human-in-the-loop design, and regulatory standards. Together, these insights provide a practical foundation for the safe and effective integration of LLMs into high-stakes IR workflows, offering value to clinicians, investigators, and developers alike.

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