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      AI education for clinicians

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          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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          Foundation models for generalist medical artificial intelligence

          Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad … (2023)
          Article 0 comments Cited 274 times – based on 0 reviews     Review now
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            Medical students' attitude towards artificial intelligence: a multicentre survey

            D. Pinto dos Santos, D. Giese, S. Brodehl … (2018)
            To assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine.
            Article 0 comments Cited 217 times – based on 0 reviews     Review now
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              Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis

              Urs J. Muehlematter, Paola Daniore, Kerstin N. Vokinger (2021)
              There has been a surge of interest in artificial intelligence and machine learning (AI/ML)-based medical devices. However, it is poorly understood how and which AI/ML-based medical devices have been approved in the USA and Europe. We searched governmental and non-governmental databases to identify 222 devices approved in the USA and 240 devices in Europe. The number of approved AI/ML-based devices has increased substantially since 2015, with many being approved for use in radiology. However, few were qualified as high-risk devices. Of the 124 AI/ML-based devices commonly approved in the USA and Europe, 80 were first approved in Europe. One possible reason for approval in Europe before the USA might be the potentially relatively less rigorous evaluation of medical devices in Europe. The substantial number of approved devices highlight the need to ensure rigorous regulation of these devices. Currently, there is no specific regulatory pathway for AI/ML-based medical devices in the USA or Europe. We recommend more transparency on how devices are regulated and approved to enable and improve public trust, efficacy, safety, and quality of AI/ML-based medical devices. A comprehensive, publicly accessible database with device details for Conformité Européene (CE)-marked medical devices in Europe and US Food and Drug Administration approved devices is needed.
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                Author and article information

                Contributors
                Tim Schubert
                Journal
                Journal ID (nlm-ta): eClinicalMedicine
                Journal ID (iso-abbrev): EClinicalMedicine
                Title: eClinicalMedicine
                Publisher: Elsevier
                ISSN (Electronic): 2589-5370
                Publication date PMC-release: 06 December 2024
                Publication date Collection: January 2025
                Publication date (Electronic): 06 December 2024
                Volume: 79
                Electronic Location Identifier: 102968
                Affiliations
                [a ]Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
                [b ]Medical Faculty, Heidelberg University, Germany
                [c ]Faculty of Medicine, KU Leuven, Leuven, Belgium
                [d ]Departments of Anesthesiology and Critical Care Medicine, Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
                [e ]School of Clinical Medicine, University of Cambridge, Cambridge, UK
                [f ]The Cambridge Centre for AI in Medicine, Cambridge, UK
                [g ]Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
                Author notes
                [∗ ]Corresponding author. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK. tim.schubert@ 123456stud.uni-heidelberg.de
                [h]

                These authors contributed equally to this article.

                Article
                Publisher Item ID: S2589-5370(24)00547-9 Publisher ID: 102968
                DOI: 10.1016/j.eclinm.2024.102968
                PMC ID: 11667627
                PubMed ID: 39720600
                SO-VID: 57095e93-f66f-4094-9266-74870e2afa51
                Copyright © © 2024 The Author(s)
                License:

                This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

                History
                Date received : 2 June 2024
                Date revision received : 6 November 2024
                Date accepted : 13 November 2024
                Categories
                Subject: Viewpoint

                Keywords: artificial intelligence,machine learning,medical education,clinicians,framework
                Data availability:
                Keywords: artificial intelligence, machine learning, medical education, clinicians, framework

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