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Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study

BACKGROUND: The use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very l...

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Autores principales: Laupichler, Matthias C., Hadizadeh, Dariusch R., Wintergerst, Maximilian W. M., von der Emde, Leon, Paech, Daniel, Dick, Elizabeth A., Raupach, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672614/
https://www.ncbi.nlm.nih.gov/pubmed/36397110
http://dx.doi.org/10.1186/s12909-022-03866-x
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author Laupichler, Matthias C.
Hadizadeh, Dariusch R.
Wintergerst, Maximilian W. M.
von der Emde, Leon
Paech, Daniel
Dick, Elizabeth A.
Raupach, Tobias
author_facet Laupichler, Matthias C.
Hadizadeh, Dariusch R.
Wintergerst, Maximilian W. M.
von der Emde, Leon
Paech, Daniel
Dick, Elizabeth A.
Raupach, Tobias
author_sort Laupichler, Matthias C.
collection PubMed
description BACKGROUND: The use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very little room for new topics that were not included before. We present a flipped classroom course designed to give undergraduate medical students an elaborated first impression of AI and to increase their “AI readiness”. METHODS: The course was tested and evaluated at Bonn Medical School in Germany with medical students in semester three or higher and consisted of a mixture of online self-study units and online classroom lessons. While the online content provided the theoretical underpinnings and demonstrated different perspectives on AI in medical imaging, the classroom sessions offered deeper insight into how “human” diagnostic decision-making differs from AI diagnoses. This was achieved through interactive exercises in which students first diagnosed medical image data themselves and then compared their results with the AI diagnoses. We adapted the “Medical Artificial Intelligence Scale for Medical Students” to evaluate differences in “AI readiness” before and after taking part in the course. These differences were measured by calculating the so called “comparative self-assessment gain” (CSA gain) which enables a valid and reliable representation of changes in behaviour, attitudes, or knowledge. RESULTS: We found a statistically significant increase in perceived AI readiness. While values of CSA gain were different across items and factors, the overall CSA gain regarding AI readiness was satisfactory. CONCLUSION: Attending a course developed to increase knowledge about AI in medical imaging can increase self-perceived AI readiness in medical students. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-022-03866-x.
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spelling pubmed-96726142022-11-18 Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study Laupichler, Matthias C. Hadizadeh, Dariusch R. Wintergerst, Maximilian W. M. von der Emde, Leon Paech, Daniel Dick, Elizabeth A. Raupach, Tobias BMC Med Educ Research BACKGROUND: The use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very little room for new topics that were not included before. We present a flipped classroom course designed to give undergraduate medical students an elaborated first impression of AI and to increase their “AI readiness”. METHODS: The course was tested and evaluated at Bonn Medical School in Germany with medical students in semester three or higher and consisted of a mixture of online self-study units and online classroom lessons. While the online content provided the theoretical underpinnings and demonstrated different perspectives on AI in medical imaging, the classroom sessions offered deeper insight into how “human” diagnostic decision-making differs from AI diagnoses. This was achieved through interactive exercises in which students first diagnosed medical image data themselves and then compared their results with the AI diagnoses. We adapted the “Medical Artificial Intelligence Scale for Medical Students” to evaluate differences in “AI readiness” before and after taking part in the course. These differences were measured by calculating the so called “comparative self-assessment gain” (CSA gain) which enables a valid and reliable representation of changes in behaviour, attitudes, or knowledge. RESULTS: We found a statistically significant increase in perceived AI readiness. While values of CSA gain were different across items and factors, the overall CSA gain regarding AI readiness was satisfactory. CONCLUSION: Attending a course developed to increase knowledge about AI in medical imaging can increase self-perceived AI readiness in medical students. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-022-03866-x. BioMed Central 2022-11-18 /pmc/articles/PMC9672614/ /pubmed/36397110 http://dx.doi.org/10.1186/s12909-022-03866-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Laupichler, Matthias C.
Hadizadeh, Dariusch R.
Wintergerst, Maximilian W. M.
von der Emde, Leon
Paech, Daniel
Dick, Elizabeth A.
Raupach, Tobias
Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title_full Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title_fullStr Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title_full_unstemmed Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title_short Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study
title_sort effect of a flipped classroom course to foster medical students’ ai literacy with a focus on medical imaging: a single group pre-and post-test study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672614/
https://www.ncbi.nlm.nih.gov/pubmed/36397110
http://dx.doi.org/10.1186/s12909-022-03866-x
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