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Grounded in reality: artificial intelligence in medical education

BACKGROUND: In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates co...

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Autores principales: Krive, Jacob, Isola, Miriam, Chang, Linda, Patel, Tushar, Anderson, Max, Sreedhar, Radhika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234762/
https://www.ncbi.nlm.nih.gov/pubmed/37273962
http://dx.doi.org/10.1093/jamiaopen/ooad037
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author Krive, Jacob
Isola, Miriam
Chang, Linda
Patel, Tushar
Anderson, Max
Sreedhar, Radhika
author_facet Krive, Jacob
Isola, Miriam
Chang, Linda
Patel, Tushar
Anderson, Max
Sreedhar, Radhika
author_sort Krive, Jacob
collection PubMed
description BACKGROUND: In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum. METHODS: We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety. RESULTS: This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice. CONCLUSIONS: We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios.
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spelling pubmed-102347622023-06-02 Grounded in reality: artificial intelligence in medical education Krive, Jacob Isola, Miriam Chang, Linda Patel, Tushar Anderson, Max Sreedhar, Radhika JAMIA Open Research and Applications BACKGROUND: In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum. METHODS: We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety. RESULTS: This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice. CONCLUSIONS: We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios. Oxford University Press 2023-06-01 /pmc/articles/PMC10234762/ /pubmed/37273962 http://dx.doi.org/10.1093/jamiaopen/ooad037 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Krive, Jacob
Isola, Miriam
Chang, Linda
Patel, Tushar
Anderson, Max
Sreedhar, Radhika
Grounded in reality: artificial intelligence in medical education
title Grounded in reality: artificial intelligence in medical education
title_full Grounded in reality: artificial intelligence in medical education
title_fullStr Grounded in reality: artificial intelligence in medical education
title_full_unstemmed Grounded in reality: artificial intelligence in medical education
title_short Grounded in reality: artificial intelligence in medical education
title_sort grounded in reality: artificial intelligence in medical education
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234762/
https://www.ncbi.nlm.nih.gov/pubmed/37273962
http://dx.doi.org/10.1093/jamiaopen/ooad037
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