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Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide.

Joshua D Brown1, Leon Lenchik2, Fayhaa Doja3

  • 1Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 (J.D.B.).

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Summary
This summary is machine-generated.

This guide helps radiology researchers use Large Language Models (LLMs) in their work. LLMs can boost research efficiency and innovation while addressing ethical considerations for scientific integrity.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology Research Methodologies

Background:

  • Large Language Models (LLMs) are increasingly adopted in radiology research.
  • The Association for Academic Radiology's Radiology Research Alliance formed a Task Force to guide LLM integration.
  • LLM adoption presents challenges, particularly for those new to artificial intelligence (AI).

Purpose of the Study:

  • To provide a guide for the responsible adoption of LLM technologies in radiology research.
  • To outline approaches for leveraging LLMs across all research phases.
  • To address prompt engineering and ethical considerations for AI in scientific research.

Main Methods:

  • Review of LLM applications in literature reviews, research question generation, data analysis, and manuscript drafting.
  • Exploration of prompt engineering techniques for effective LLM interaction.
  • Discussion of ethical concerns to maintain scientific integrity.

Main Results:

  • LLMs can significantly enhance efficiency in various research tasks.
  • Effective prompt engineering is crucial for optimal LLM performance.
  • Addressing ethical considerations is vital for responsible AI implementation.

Conclusions:

  • Integrating human expertise with AI-driven efficiency through LLMs can foster innovation in radiology.
  • LLM adoption has the potential to advance knowledge and improve patient care.
  • Responsible implementation is key to maximizing the benefits of LLMs in radiology research.