Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Mapping the use of artificial intelligence in medical education: a scoping review
91
Zitationen
6
Autoren
2025
Jahr
Abstract
INTRODUCTION: The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically. OBJECTIVE: This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption. MATERIALS AND METHODS: A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. RESULTS: A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject. CONCLUSIONS: This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.687 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.591 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.114 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.867 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.