Open AccessIntroducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees
Author(s) -
Jared Tschirhart,
Amadene Woolsey,
Jamila Skinner,
Khadija Ahmed,
Courtney Fleming,
Justin Kim,
Chintan Dave,
Robert Arntfield
Publication year - 2022
Publication title -
canadian medical education journal
Language(s) - English
Resource type - Journals
ISSN - 1923-1202
DOI - 10.36834/cmej.75074
Subject(s) - competence (human resources) , fluency , artificial intelligence , humanities , medical education , psychology , library science , computer science , medicine , philosophy , mathematics education , social psychology
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.