A Hitchhiker's Guide to Good Prompting Practices for Large Language Models in Radiology Article Swipe
Satvik Tripathi
,
Dana Alkhulaifat
,
Shawn Lyo
,
Rithvik Sukumaran
,
Bolin Li
,
Vedant Acharya
,
Rafe McBeth
,
Tessa S. Cook
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.jacr.2025.02.051
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.jacr.2025.02.051
Large language models (LLMs) are reshaping radiology through their advanced capabilities in tasks such as medical report generation and clinical decision support. However, their effectiveness is heavily influenced by prompt engineering-the design of input prompts that guide the model's responses. This review aims to illustrate how different prompt engineering techniques, including zero-shot, one-shot, few-shot, chain of thought, and tree of thought, affect LLM performance in a radiology context. In addition, we explore the impact of prompt complexity and temperature settings on the relevance and accuracy of model outputs. This article highlights the importance of precise and iterative prompt design to enhance LLM reliability in radiology, emphasizing the need for methodological rigor and transparency to drive progress and ensure ethical use in health care.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jacr.2025.02.051
- https://www.jacr.org/article/S1546-1440(25)00156-5/pdf
- OA Status
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- Cited By
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- References
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- OpenAlex ID
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A Hitchhiker's Guide to Good Prompting Practices for Large Language Models in RadiologyWork title
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reviewOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-01Full publication date if available
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Satvik Tripathi, Dana Alkhulaifat, Shawn Lyo, Rithvik Sukumaran, Bolin Li, Vedant Acharya, Rafe McBeth, Tessa S. CookList of authors in order
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https://doi.org/10.1016/j.jacr.2025.02.051Publisher landing page
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https://www.jacr.org/article/S1546-1440(25)00156-5/pdfDirect link to full text PDF
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