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A Hitchhiker's Guide to Good Prompting Practices for Large Language Models in Radiology.

Satvik Tripathi1, Dana Alkhulaifat2, Shawn Lyo3

  • 1Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiation Oncology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania.

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This summary is machine-generated.

Prompt engineering significantly impacts large language models (LLMs) in radiology. Optimizing prompts is crucial for accurate medical report generation and clinical decision support using LLMs.

Keywords:
Artificial intelligencelarge language modelsprompt engineering

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Informatics
  • Natural Language Processing in Healthcare

Background:

  • Large language models (LLMs) offer advanced capabilities for medical report generation and clinical decision support in radiology.
  • The performance of LLMs in healthcare is highly dependent on prompt engineering, which involves designing effective input prompts.
  • Current research needs to explore how various prompt engineering techniques influence LLM outcomes in a clinical radiology setting.

Purpose of the Study:

  • To review and illustrate the impact of different prompt engineering techniques on LLM performance in radiology.
  • To analyze how prompt complexity and temperature settings affect the accuracy and relevance of LLM outputs in medical contexts.
  • To emphasize the necessity of rigorous and transparent prompt design for reliable and ethical LLM application in healthcare.

Main Methods:

  • Review of prompt engineering techniques including zero-shot, one-shot, few-shot, chain of thought, and tree of thought.
  • Exploration of the influence of prompt complexity and temperature parameters on LLM responses.
  • Analysis of LLM performance metrics related to relevance and accuracy in radiology tasks.

Main Results:

  • Different prompt engineering strategies yield varying levels of effectiveness for LLMs in radiology.
  • Prompt complexity and temperature settings are critical factors influencing the quality of LLM-generated radiology reports and decision support.
  • Iterative and precise prompt design is essential for maximizing LLM reliability.

Conclusions:

  • Effective prompt engineering is paramount for harnessing the full potential of LLMs in radiology.
  • Methodological rigor and transparency in prompt design are vital for advancing AI in healthcare.
  • Ensuring ethical use and enhancing LLM reliability requires a focus on sophisticated prompt engineering strategies.