Mapping the use of artificial intelligence in medical education: a scoping review

• Published in: BMC medical education Vol. 25; no. 1; pp. 526 - 16
• Main Authors: Rincón, Erwin Hernando Hernández, Jimenez, Daniel, Aguilar, Lizeth Alexandra Chavarro, Flórez, Juan Miguel Pérez, Tapia, Álvaro Enrique Romero, Peñuela, Claudia Liliana Jaimes
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.04.2025; BioMed Central Ltd; BMC
• Subjects: Artificial Intelligence; Artificial intelligence (AI); Clinical Competence; Computational linguistics; Curriculum; Curriculum development; Diagnostic equipment (Medical); Education; Education, Medical, Undergraduate - methods; Educational aspects; Educational evaluation; Educational research; Ethical aspects; Ethical training; Experiential learning; Group work in education; Humans; Language processing; Medical colleges; Medical Education; Medical personnel; Methods; Natural language interfaces; Team learning approach in education; Technology application; Theory of Medicine/Bioethics; Training; Undergraduate medical education (UME); Colombia; Medical education; Undergraduate medical education (UME); Experiential learning; Ethical training; Artificial intelligence (AI); Curriculum development
• ISSN: 1472-6920; 1472-6920
• DOI: 10.1186/s12909-025-07089-8

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.