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

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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.

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References

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Published

2022-10-21

How to Cite

1.
Tschirhart J, Woolsey A, Skinner J, Ahmed K, Fleming C, Kim J, Dave C, Arntfield R. 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 May 29];14(3):113-5. Available from: https://journalhosting.ucalgary.ca/index.php/cmej/article/view/75074

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