AI education for clinicians

Summary: Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and o...

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Main Authors: Tim Schubert, Tim Oosterlinck, Robert D. Stevens, Patrick H. Maxwell, Mihaela van der Schaar
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:EClinicalMedicine
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2589537024005479
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author Tim Schubert
Tim Oosterlinck
Robert D. Stevens
Patrick H. Maxwell
Mihaela van der Schaar
author_facet Tim Schubert
Tim Oosterlinck
Robert D. Stevens
Patrick H. Maxwell
Mihaela van der Schaar
author_sort Tim Schubert
collection DOAJ
description Summary: Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.
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institution Kabale University
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series EClinicalMedicine
spelling doaj-art-8cdd047a58944057bbaa8ff805f327732025-01-22T05:43:11ZengElsevierEClinicalMedicine2589-53702025-01-0179102968AI education for cliniciansTim Schubert0Tim Oosterlinck1Robert D. Stevens2Patrick H. Maxwell3Mihaela van der Schaar4Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; Medical Faculty, Heidelberg University, Germany; Institute of Human Genetics, Heidelberg University, Heidelberg, Germany; Corresponding author. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; Faculty of Medicine, KU Leuven, Leuven, BelgiumDepartments of Anesthesiology and Critical Care Medicine, Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USASchool of Clinical Medicine, University of Cambridge, Cambridge, UKDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Cambridge Centre for AI in Medicine, Cambridge, UKSummary: Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.http://www.sciencedirect.com/science/article/pii/S2589537024005479Artificial intelligenceMachine learningMedical educationCliniciansFramework
spellingShingle Tim Schubert
Tim Oosterlinck
Robert D. Stevens
Patrick H. Maxwell
Mihaela van der Schaar
AI education for clinicians
EClinicalMedicine
Artificial intelligence
Machine learning
Medical education
Clinicians
Framework
title AI education for clinicians
title_full AI education for clinicians
title_fullStr AI education for clinicians
title_full_unstemmed AI education for clinicians
title_short AI education for clinicians
title_sort ai education for clinicians
topic Artificial intelligence
Machine learning
Medical education
Clinicians
Framework
url http://www.sciencedirect.com/science/article/pii/S2589537024005479
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