Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models.

Journal: JMIR formative research
PMID: 40063849

Abstract

BACKGROUND: Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize health care quality and accessibility. At a time when improving health care quality and access remains a critical concern for countries worldwide, the ability of these models to pass medical examinations is often cited as a reason to use them for medical training and diagnosis. However, the impact of their inevitable use as a self-diagnostic tool and their role in spreading health care misinformation has not been evaluated.

Authors

  • Troy Zada
    Department of Management Sciences and Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 33279.
  • Natalie Tam
    Department of Management Sciences and Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 33279.
  • Francois Barnard
    Department of Management Sciences and Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 33279.
  • Marlize Van Sittert
    Faculty of Law, University of Toronto, Toronto, ON, Canada.
  • Venkat Bhat
    Centre for Mental Health and Krembil Research Centre, University Health Network, Toronto, ON, Canada.
  • Sirisha Rambhatla
    Computer Science Department, University of Southern California, Los Angeles, CA, U.S.A.

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