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

Jared Tschirhart; Amadene Woolsey; Jamila Skinner; Khadija Ahmed; Courtney Fleming; Justin Kim; Chintan Dave; Robert Arntfield

doi:10.36834/cmej.75074

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

Authors

Jared Tschirhart Schulich School of Medicine & Dentistry https://orcid.org/0000-0003-1313-350X
Amadene Woolsey Schulich School of Medicine and Dentistry https://orcid.org/0000-0002-0121-7952
Jamila Skinner Schulich School of Medicine and Dentistry https://orcid.org/0000-0002-0259-9710
Khadija Ahmed Schulich School of Medicine and Dentistry
Courtney Fleming Schulich School of Medicine and Dentistry https://orcid.org/0000-0002-8863-5558
Justin Kim Schulich School of Medicine and Dentistry https://orcid.org/0000-0001-5551-2466
Chintan Dave Western University
Robert Arntfield Western University https://orcid.org/0000-0003-4954-487X

DOI:

https://doi.org/10.36834/cmej.75074

Abstract

Implication Statement

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.

References

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Published

2022-10-21

How to Cite

Tschirhart J, Woolsey A, Skinner J, Ahmed K, Fleming C, Kim J, et al. Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees. Can. Med. Ed. J [Internet]. 2022 Oct. 21 [cited 2024 Dec. 21];14(3):113-5. Available from: https://journalhosting.ucalgary.ca/index.php/cmej/article/view/75074

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Issue

Vol. 14 No. 3 (2023)

Section

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