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Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees
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 grou...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Canadian Medical Education Journal
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351644/ https://www.ncbi.nlm.nih.gov/pubmed/37465748 http://dx.doi.org/10.36834/cmej.75074 |
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author | Tschirhart, Jared Woolsey, Amadene Skinner, Jamila Ahmed, Khadija Fleming, Courtney Kim, Justin Dave, Chintan Arntfield, Robert |
author_facet | Tschirhart, Jared Woolsey, Amadene Skinner, Jamila Ahmed, Khadija Fleming, Courtney Kim, Justin Dave, Chintan Arntfield, Robert |
author_sort | Tschirhart, Jared |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10351644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Canadian Medical Education Journal |
record_format | MEDLINE/PubMed |
spelling | pubmed-103516442023-07-18 Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees Tschirhart, Jared Woolsey, Amadene Skinner, Jamila Ahmed, Khadija Fleming, Courtney Kim, Justin Dave, Chintan Arntfield, Robert Can Med Educ J You Should Try This! 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. Canadian Medical Education Journal 2023-06-27 /pmc/articles/PMC10351644/ /pubmed/37465748 http://dx.doi.org/10.36834/cmej.75074 Text en © 2023 Tschirhart, Woolsey, Skinner, Ahmed, Fleming, Kim, Dave, Arntfield; licensee Synergies Partners. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Journal Systems article distributed under the terms of the Creative Commons Attribution License. (https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited. |
spellingShingle | You Should Try This! Tschirhart, Jared Woolsey, Amadene Skinner, Jamila Ahmed, Khadija Fleming, Courtney Kim, Justin Dave, Chintan Arntfield, Robert Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title | Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title_full | Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title_fullStr | Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title_full_unstemmed | Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title_short | Introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
title_sort | introducing medical students to deep learning through image labelling: a new approach to meet calls for greater artificial intelligence fluency among medical trainees |
topic | You Should Try This! |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351644/ https://www.ncbi.nlm.nih.gov/pubmed/37465748 http://dx.doi.org/10.36834/cmej.75074 |
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