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Consenso de expertos sobre la lista de competencias de aptitud en inteligencia artificial y el marco de evaluación

Meng-Chun Gong1, Hui Pan2, Hui Liu3

  • 1Guangdong Medical University,Dongguan,Guangdong 523808,China.

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
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Resumen

Este consenso describe una lista de competencias de alfabetización en inteligencia artificial (IA) y un marco de evaluación para estudiantes de medicina. Su objetivo es preparar a los futuros profesionales de la salud para un panorama médico impulsado por la IA.

Palabras clave:
inteligencia artificialaptitud en inteligencia artificialcompetencia clínicaeducación médicaestudiantes de medicina

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Área de la Ciencia:

  • Educación Médica
  • Inteligencia Artificial en la Atención Médica
  • Educación Basada en Competencias

Sus antecedentes:

  • El rápido avance de la atención médica inteligente exige la formación de profesionales médicos con alfabetización en IA.
  • Los marcos de educación médica existentes requieren adaptación para integrar las competencias de IA.
  • La educación médica basada en competencias (EMBC) proporciona una base para el desarrollo de médicos preparados para la IA.

Objetivo del estudio:

  • Desarrollar una lista integral de competencias de alfabetización en IA para estudiantes de medicina.
  • Establecer un marco de evaluación práctico para evaluar las competencias de IA en la educación médica.
  • Guiar a las facultades de medicina en la integración de la IA en sus planes de estudio para la atención médica inteligente.

Principales métodos:

  • Método Delphi de dos rondas que involucra a un panel multidisciplinario de expertos.
  • Revisión sistemática de la literatura para informar el desarrollo de competencias.
  • Integración de modelos de competencias y la pirámide de competencia clínica de Miller.

Principales resultados:

  • Una lista de competencias de alfabetización en IA de 21 indicadores para estudiantes de medicina, categorizada en conocimiento (8), habilidades (8) y actitudes (5).
  • Un sistema de evaluación propuesto que incluye pruebas estandarizadas, pruebas de juicio situacional y exámenes clínicos objetivos estructurados basados en escenarios de IA.
  • Una estrategia de evaluación longitudinal recomendada en las fases de admisión, preclínica y clínica.

Conclusiones:

  • La lista de competencias de IA y el marco de evaluación desarrollados poseen rigor científico y aplicabilidad práctica.
  • Este consenso sirve como una referencia crucial para las facultades de medicina que integran la IA en la educación.
  • Apoya el cultivo de talentos médicos compuestos para la era de la atención médica inteligente.