Prompt Engineering for Large Language Models in Interventional Radiology

Abstract

Prompt engineering plays a crucial role in optimizing artificial intelligence (AI) and large language model (LLM) outputs by refining input structure, a key factor in medical applications where precision and reliability are paramount. This Clinical Perspective provides an overview of prompt-engineering techniques and their relevance to interventional radiology (IR). It explores key strategies, including zero-shot, one-or few-shot, chain-of-thought, tree-of-thought, self-consistency, and directional stimulus prompting, showing their application in IR-specific contexts. Practical examples illustrate how these techniques can be effectively structured for workplace and clinical use. Additionally, this article discusses best practices for designing effective prompts and addresses challenges in the clinical use of generative AI, including data privacy and regulatory concerns. It concludes with an outlook on the future of generative AI in IR, highlighting advances including retrieval-augmented generation, domain-specific LLMs, and multimodal models.

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