Mapping the use of artificial intelligence in medical education: a scoping review
Open accessAbstract
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.
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Global South students report substantially less AI integration in curricula and lower preparedness levels.
“Global South students report substantially less AI integration in curricula and lower preparedness levels (Busch et al., 2024; Rincón et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Rincón et al., 2025
The Discussion sections on global discrepancies and regional barriers describe less exposure to interdisciplinary AI applications in Nigeria and more limited access to AI educational tools in low- and middle-income countries. They do not establish that Global South students themselves report substantially less curricular integration or lower preparedness than students elsewhere; the Results also report Canadian students feeling unprepared. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFormal longitudinal AI teaching in emergency medicine is uncommon.
“Formal longitudinal AI teaching is uncommon,26, 27, 28,36, 37, 38, 39”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [38]
The Introduction says AI integration into medical curricula remains insufficient, and the Discussion section on curriculum delivery describes a lack of standardized AI education. That supports the broad point that formal AI teaching is not well established. The review focuses on undergraduate medical education, however, and does not establish that longitudinal teaching is uncommon specifically in emergency medicine; its mention of scarce longitudinal studies concerns evaluation, not teaching duration. No page numbers are provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLimited curricular time, scarce faculty expertise, and competing priorities leave a readiness gap in AI education.
“limited curricular time, scarce faculty expertise, and competing priorities leave a readiness gap.27, 28, 29,36, 37, 38, 39”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [38]
The Introduction (page unavailable) describes a gap in AI education that leaves future clinicians underprepared. The Discussion’s sections on faculty readiness and training deficits (pages unavailable) identify limited faculty AI training as a barrier. The passages do not identify limited curricular time or competing priorities as causes of that gap. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMore and more AI-based solutions are being integrated into emergency department workflows.
“more and more AI-based solutions are being integrated into emergency department (ED) workflows,22,23,38,40”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [38]
The Introduction says AI is increasingly used in clinical decision-making, including diagnostic imaging, predictive analytics, and patient management. It does not say that AI-based solutions are increasingly being integrated into emergency department workflows; the review focuses on medical education rather than that clinical setting. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe mismatch between foundational AI knowledge and practice realities raises concern about patient safety.
“this mismatch in foundational knowledge and practice realities raises concern about patient safety.20,22,23,27,38,40”
From Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce (2026) · cites this paper as [38]
These scoping reviews largely describe broad "AI literacy" themes or program-level content rather than systematically extracting and organizing competency- and learning-objective-level outcomes for undergraduate medical education.
“but these syntheses largely describe broad “AI literacy” themes or program-level content rather than systematically extracting and organizing competency- and learning-objective-level outcomes for undergraduate medical education (UME).”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [27]
Several scoping reviews have examined AI within medical education.
“Several scoping reviews have examined AI within medical education23–28”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [27]
Prior work has articulated high-level domains.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [27]
Prior work has emphasized the need for clearer competency frameworks.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [27]
Prior work has mapped curricula and interventions.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [27]
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- Authors
- Rincón, E. H. H., Jimenez, D., Aguilar, L. A. C., Flórez, J. M. P., Tapia, Á. E. R., & Peñuela, C. L. J.
- Affiliations
- Universidad de La Sabana (CO)
- Corresponding author
- Rincón, Erwin Hernando Hernández
- ORCID
- Venue
- BMC Medical Education
- Published
- 2025-04-12
- Volume
- 25
- Issue
- 1
- Pages
- 526-526
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC11993958
- OpenAlex
- W4409390925
- Cited by
- 145
- References
- 53
- Citations by year
- 2026: 102 · 2025: 43
- FWCI
- 8.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Education (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad de La Sabana (MED-342-2023)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in medical education, undergraduate medical education, digital skills, AI ethics education, transversal competences, clinical decision support, AI adoption barriers
- MeSH terms
- Artificial Intelligence, Clinical Competence, Curriculum, Education, Medical, Undergraduate, Humans
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