How can artificial intelligence transform the training of medical students and physicians?
Rattachement africain : sg, cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Advances in artificial intelligence (AI), particularly generative AI, hold promise for transforming medical education and physician training in response to increasing health-care demands and shortages in the global health-care workforce. Meanwhile, challenges remain in the effective and equitable integration of AI technology into medical education and physician training worldwide. This Viewpoint explores the opportunities and challenges of such an integration. We study the evolving role of AI in medical education, its potential to enhance high-fidelity clinical training, and its contribution to research training using real-world examples. We also highlight ethical concerns, particularly the unclear boundaries of appropriate use of AI and call for clear guidelines to govern the integration of AI into medical education and physician training. Furthermore, this Viewpoint discusses practical constraints, including human, financial, and resource constraints, in AI integration, and emphasises the need for comprehensive cost evaluations and collaborative funding models to support the sustainable implementation of AI integration. A tight collaborative network between health-care institutions and systems, medical schools and universities, industry partners, and education and health-care regulatory agencies could lead to an AI-transformed medical education and physician training scheme that ultimately supports the adoption and integration of AI into clinical medicine and potentially brings about tangible improvements in global health-care delivery.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- How can artificial intelligence transform the training of medical students and physicians?
- Date Crossref
- 01/10/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Duke-NUS Medical School pays non établi dans la noticeUniversité ou école supérieure
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Singapore National Eye Center pays non établi dans la noticeÉtablissement de santé
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Sun Yat-sen University Research Centre of Big Data and Artificial Intelligence for Medicine pays non établi dans la noticeUniversité ou école supérieure
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Singapore Eye Research Institute pays non établi dans la noticeStructure de recherche
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Beijing Tsinghua Chang Gung Hospital pays non établi dans la noticeÉtablissement de santé
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Tsinghua University Singapore Eye Research Institute pays non établi dans la noticeUniversité ou école supérieure
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Duke-National University of Singapore (NUS) AI + Medical Sciences Initiative Singapore pays non établi dans la noticeUniversité ou école supérieure
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Stanford University Ophthalmology and Visual Science Academic Clinical Program (EYE ACP) pays non établi dans la noticeUniversité ou école supérieure
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Yong Loo Lin School of Medicine Ophthalmology and Visual Science Academic Clinical Program (EYE ACP) pays non établi dans la noticeUniversité ou école supérieure
Duke-NUS Medical School, Singapore National Eye Center et Research Centre of Big Data and Artificial Intelligence for Medicine — Sun Yat-sen University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.