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

Erwin Hernando Hernández Rincón; Daniel Jimenez; Lizeth Alexandra Chavarro Aguilar; Juan Miguel Pérez Flórez; Álvaro Enrique Romero Tapia; Claudia Liliana Jaimes Peñuela

doi:10.1186/s12909-025-07089-8

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

BMC Med Educ. 2025 Apr 12;25(1):526. doi: 10.1186/s12909-025-07089-8.

Authors

Erwin Hernando Hernández Rincón¹, Daniel Jimenez², Lizeth Alexandra Chavarro Aguilar², Juan Miguel Pérez Flórez², Álvaro Enrique Romero Tapia³, Claudia Liliana Jaimes Peñuela⁴

Affiliations

¹ Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia. erwinhr@unisabana.edu.co.
² Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia.
³ Department of Psychiatry and Mental Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia.
⁴ Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia.

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.

Keywords: Artificial intelligence (AI); Curriculum development; Ethical training; Experiential learning; Medical education; Undergraduate medical education (UME).

Publication types

Scoping Review

MeSH terms

Artificial Intelligence*
Clinical Competence
Curriculum*
Education, Medical, Undergraduate* / methods
Humans

Abstract

Publication types

MeSH terms

Grants and funding