Inteligencia artificial en la formación clínica práctica de estudiantes de medicina de pregrado: una revisión de alcance de aplicaciones, resultados y brechas.

Juan Jose Bonilla Mejia; Tomás David Cortés Fuenzalida; Diego Hernán Polanco Aliaga; Carlos Martínez Carrillo; Álvaro Andrés Herrera Alcaíno

doi:10.6018/edumed.710071

Autores

Juan Jose Bonilla Mejia Escuela de Medicina, Facultad de Medicina, Universidad de Chile https://orcid.org/0009-0007-4086-083X
Tomás David Cortés Fuenzalida Escuela de Medicina, Facultad de Medicina, Universidad de Chile. Santiago, Chile https://orcid.org/0009-0008-4768-2746
Diego Hernán Polanco Aliaga Escuela de Medicina, Facultad de Medicina, Universidad de Chile. Santiago, Chile https://orcid.org/0009-0008-9502-465X
Carlos Martínez Carrillo Escuela de Medicina, Facultad de Medicina, Universidad de Chile. Santiago, Chile https://orcid.org/0009-0001-1136-1026
Álvaro Andrés Herrera Alcaíno Departamento de Educación en Ciencias de la Salud, Facultad de Medicina, Universidad de Chile https://orcid.org/0009-0007-4861-2144

DOI: https://doi.org/10.6018/edumed.710071

Palavras-chave: Inteligencia artificial, Modelos de Lenguaje a Gran Escala, Educación médica, Competencia clínica, Estudiantes de medicina, Simulación clínica, Revisión de alcance

Resumo

Objetivo: Mapear y sintetizar la evidencia disponible sobre el uso de inteligencia artificial (IA), incluidos los modelos de lenguaje de gran escala (LLM) y las herramientas generativas, en la formación clínica práctica de estudiantes de medicina de pregrado. Metodología: Se realizó una scoping review siguiendo el marco metodológico de Arksey y O’Malley, y se reportó de acuerdo con PRISMA-ScR. La búsqueda bibliográfica se llevó a cabo el 28 de enero de 2026 en PubMed/MEDLINE, Scopus y Web of Science Core Collection. Se incluyeron estudios empíricos publicados desde 2021 en adelante, en inglés, español o portugués, que evaluaran intervenciones educativas basadas en IA en estudiantes de medicina de pregrado, en contextos de formación práctica clínica supervisada en escenarios reales y/o simulados. Resultados: Se identificaron 2112 registros, de los cuales 789 fueron eliminados por duplicación. Tras el cribado y la evaluación de texto completo, se incluyeron 24 estudios. La evidencia se concentró en escenarios simulados o estructurados y en dominios como entrevista clínica/comunicación, razonamiento clínico y habilidades técnicas o procedimentales. Los LLM y las herramientas generativas fueron las tecnologías más frecuentemente estudiadas. Un subconjunto de estudios comparativos reportó resultados comparables o, en determinados dominios y contextos específicos, favorables para intervenciones basadas en IA; sin embargo, la heterogeneidad metodológica de los comparadores, outcomes y diseños impide extraer conclusiones agregadas sobre efectividad, y la evidencia se concentró principalmente en desenlaces educativos inmediatos. Conclusiones: La IA muestra potencial como herramienta complementaria para ampliar la práctica deliberada, estandarizar la retroalimentación y apoyar experiencias de aprendizaje más accesibles y, en algunos casos, más personalizadas. No obstante, persisten limitaciones relacionadas con la heterogeneidad metodológica, la escasa evaluación en contextos clínicos reales y la falta de seguimiento longitudinal, por lo que se requieren estudios más robustos y marcos éticos y pedagógicos claros que orienten su integración responsable en la educación médica de pregrado.

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