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Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews

BACKGROUND: The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current li...

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Detalles Bibliográficos
Autores principales: Weidener, Lukas, Fischer, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167581/
https://www.ncbi.nlm.nih.gov/pubmed/36946094
http://dx.doi.org/10.2196/46428
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author Weidener, Lukas
Fischer, Michael
author_facet Weidener, Lukas
Fischer, Michael
author_sort Weidener, Lukas
collection PubMed
description BACKGROUND: The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. OBJECTIVE: We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. METHODS: For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. RESULTS: The qualitative content analysis led to the formation of three main categories (“Knowledge,” “Interpretation,” and “Application”) with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one’s related competencies. CONCLUSIONS: The analyzed expert interviews’ results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.
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spelling pubmed-101675812023-05-10 Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews Weidener, Lukas Fischer, Michael JMIR Med Educ Original Paper BACKGROUND: The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. OBJECTIVE: We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. METHODS: For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. RESULTS: The qualitative content analysis led to the formation of three main categories (“Knowledge,” “Interpretation,” and “Application”) with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one’s related competencies. CONCLUSIONS: The analyzed expert interviews’ results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula. JMIR Publications 2023-04-24 /pmc/articles/PMC10167581/ /pubmed/36946094 http://dx.doi.org/10.2196/46428 Text en ©Lukas Weidener, Michael Fischer. Originally published in JMIR Medical Education (https://mededu.jmir.org), 24.04.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Weidener, Lukas
Fischer, Michael
Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title_full Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title_fullStr Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title_full_unstemmed Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title_short Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
title_sort artificial intelligence teaching as part of medical education: qualitative analysis of expert interviews
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167581/
https://www.ncbi.nlm.nih.gov/pubmed/36946094
http://dx.doi.org/10.2196/46428
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