Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees.

Journal: Canadian medical education journal
Published Date: Jun 27, 2023

Abstract

Our approach addresses the urgent need for AI experience for the doctors of tomorrow. Through a medical education-focused approach to data labelling, we have fostered medical student competence in medical imaging and AI. We envision our framework being applied at other institutions and academic groups to develop robust labelling programs for research endeavours. Application of our approach to core visual modalities within medicine (e.g. interpretation of ECGs, diagnostic imaging, dermatologic findings) can lead to valuable student experience and competence in domains that feature prominently in clinical practice, while generating much needed data in fields that are ripe for AI integration.

Authors

  • Jared Tschirhart
    Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5C1, Canada.
  • Amadene Woolsey
    Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.
  • Jamila Skinner
    Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.
  • Khadija Ahmed
    Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.
  • Courtney Fleming
    Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.
  • Justin Kim
    Department of Medicine, University of Florida, Gainesville, FL, United States of America.
  • Chintan Dave
    Division of Critical Care Medicine, Western University, London, ON N6A 5C1, Canada.
  • Robert Arntfield
    Department of Critical Care Medicine, Western University, London, Ontario, Canada.

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