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Inteligencia Artificial para la Supervisión: IA en la Acreditación del Desarrollo Profesional Continuo
1Dr. McMahon: President and CEO, Accreditation Council for Continuing Medical Education, Chicago, IL; and Adjunct Professor of Medicine and Medical Education, Northwestern University Feinberg School of Medicine, Chicago, IL.
The Journal of continuing education in the health professions
|December 22, 2025
Resumen
La inteligencia artificial (IA) puede mejorar la supervisión del desarrollo profesional continuo (DPC) al mejorar la eficiencia y la integración de datos. La implementación responsable de la IA por parte de los organismos de acreditación es crucial para mantener sistemas de DPC confiables y creíbles.
Área de la Ciencia:
- Educación en Profesiones de la Salud
- Inteligencia Artificial
- Ciencia Regulatoria
Sus antecedentes:
- La supervisión del desarrollo profesional continuo (DPC) es fundamental para garantizar una educación confiable y objetiva en las profesiones de la salud.
- La aplicación de la inteligencia artificial (IA) en la supervisión del DPC es un área poco explorada.
Objetivo del estudio:
- Examinar las aplicaciones potenciales de la IA en la supervisión de la acreditación para el DPC.
- Analizar cómo la IA puede apoyar la eficiencia, la coherencia y la obtención de información en las funciones regulatorias.
- Enfatizar la necesidad del juicio humano y la gobernanza en la supervisión asistida por IA.
Principales métodos:
- Síntesis de la literatura actual y la orientación profesional.
- Análisis de las aplicaciones de la IA en las funciones regulatorias de los organismos de acreditación.
- Examen de las oportunidades y los riesgos asociados con la IA en la supervisión del DPC.
Principales resultados:
- La IA puede revisar materiales de reacreditación, identificar actividades de alto riesgo, sintetizar tendencias, vincular datos y evaluar el uso responsable de la IA.
- Los beneficios potenciales incluyen una mayor eficiencia y una mejor integración de datos.
- Los desafíos incluyen la precisión, la equidad, la transparencia, la seguridad y la confianza pública.
Conclusiones:
- Los sistemas de acreditación pueden modelar el uso responsable de la IA para avanzar en el aprendizaje profesional y salvaguardar la integridad del DPC.
- La integración responsable de la IA requiere principios de rendición de cuentas, transparencia, equidad y seguridad.
- La IA puede mejorar la eficiencia y la confiabilidad de la supervisión, reforzando la credibilidad global del DPC.
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