Assessing AI-Powered Patient Education: A Case Study in Radiology.

Journal: Academic radiology
Published Date: Sep 14, 2023

Abstract

RATIONALE AND OBJECTIVES: With recent advancements in the power and accessibility of artificial intelligence (AI) Large Language Models (LLMs) patients might increasingly turn to these platforms to answer questions regarding radiologic examinations and procedures, despite valid concerns about the accuracy of information provided. This study aimed to assess the accuracy and completeness of information provided by the Bing Chatbot-a LLM powered by ChatGPT-on patient education for common radiologic exams.

Authors

  • Ian J Kuckelman
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705. Electronic address: kuckelman@wisc.edu.
  • Paul H Yi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: Pyi10@jhmi.edu.
  • Molinna Bui
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.
  • Ifeanyi Onuh
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.
  • Jade A Anderson
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.
  • Andrew B Ross
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.

Keywords

External Resources

Popular Topics
Recent Journals