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When Seeing Isn't Believing: Navigating Visual Health Misinformation through Library Instruction.

Kelsey CowlesRebekah S MillerRachel Suppok
Published in: Medical reference services quarterly (2024)
Visual misinformation poses unique challenges to public health due to its potential for persuasiveness and rapid spread on social media. In this article, librarians at the University of Pittsburgh Health Sciences Library System identify four types of visual health misinformation: misleading graphs and charts, out of context visuals, image manipulation in scientific publications, and AI-generated images and videos. To educate our campus's health sciences audience and wider community on these topics, we have developed a range of instruction about visual health misinformation. We describe our strategies and provide suggestions for implementing visual misinformation programming for a variety of audiences.
Keyphrases
  • social media
  • public health
  • health information
  • healthcare
  • mental health
  • deep learning
  • artificial intelligence
  • machine learning
  • global health