Artificial intelligence in the practical clinical training of undergraduate medical students: a scoping review of applications, outcomes, and gaps.

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DOI: https://doi.org/10.6018/edumed.710071
Keywords: Inteligencia artificial, Modelos de Lenguaje a Gran Escala, Educación médica, Competencia clínica, Estudiantes de medicina, Simulación clínica, Revisión de alcance

Abstract

Objective: To map and synthesize the available evidence on the use of artificial intelligence (AI), including large language models (LLMs) and generative tools, in the practical clinical training of undergraduate medical students. Methods: A scoping review was conducted following the Arksey and O’Malley methodological framework and reported in accordance with PRISMA-ScR. The literature search was carried out on January 28, 2026, in PubMed/MEDLINE, Scopus, and Web of Science Core Collection. Empirical studies published from 2021 onward in English, Spanish, or Portuguese were included if they evaluated AI-based educational interventions for undergraduate medical students in supervised practical clinical training in real and/or simulated settings. Results: A total of 2,112 records were identified, of which 789 were removed as duplicates. After screening and full-text assessment, 24 studies were included. The evidence was concentrated in simulated or structured settings and in domains such as clinical interviewing/communication, clinical reasoning, and technical or procedural skills. LLMs and generative tools were the most frequently studied technologies. A subset of comparative studies reported outcomes that were comparable or, in specific domains and contexts, favorable to AI-based interventions; however, the methodological heterogeneity of comparators, outcomes, and study designs precluded drawing aggregated conclusions about effectiveness, and the evidence was focused mainly on immediate educational outcomes. Conclusions: AI shows potential as a complementary tool to expand deliberate practice, standardize feedback, and support more accessible and, in some cases, more personalized learning experiences. Nevertheless, important limitations remain, including methodological heterogeneity, limited evaluation in real clinical settings, and the lack of longitudinal follow-up. More robust studies and clear ethical and pedagogical frameworks are needed to guide its responsible integration into undergraduate medical education.

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Published
05-05-2026
How to Cite
Bonilla Mejia, J. J., Cortés Fuenzalida, T. D., Polanco Aliaga, D. H., Martínez Carrillo, C., & Herrera Alcaíno, Álvaro A. (2026). Artificial intelligence in the practical clinical training of undergraduate medical students: a scoping review of applications, outcomes, and gaps. Spanish Journal of Medical Education, 7(3). https://doi.org/10.6018/edumed.710071
Issue
Vol. 7 No. 3 (2026): Spanish Journal of Medical Education
Section
Artículos

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