Advanced Prompt Engineering for Large Language Models in Interventional Radiology: Practical Strategies and Future Perspectives
Bibliographic record
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 show 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".