Validación clínica de la inteligencia artificial en imágenes musculoesqueléticas: una revisión narrativa estructurada de resultados, modos de fallo y preparación para la implementación

Autores/as

  • Fabriccio J. Visconti-Lopez Universidad Científica del Sur, Lima, Peru. https://orcid.org/0000-0002-8056-2112
  • Ivan David Lozada-Martínez Center for Meta-Research and Scientometrics in Biomedical Sciences, Barranquilla, Colombia; Biomedical Scientometrics and Evidence-Based Research Unit, Department of Health Sciences, Universidad de la Costa, Barranquilla, Colombia; Clínica Iberoamérica, Barranquilla, Colombia. https://orcid.org/0000-0002-1960-7334

DOI:

https://doi.org/10.21142/mecp.2026.1.1.e011

Palabras clave:

Inteligencia Artificial, Aprendizaje Automático, Aprendizaje Profundo, Diagnóstico por Imagen, Sistema Musculoesquelético, Radiografía

Resumen

La inteligencia artificial (IA) está ingresando rápidamente en la imagenología musculoesquelética (MEQ), aunque la adopción clínica a menudo supera la evidencia de beneficio en el mundo real, ampliando la brecha entre las afirmaciones de rendimiento diagnóstico y los desenlaces importantes para los pacientes. En esta revisión narrativa estructurada, sintetizamos la evidencia publicada y las guías de reporte relevantes para la validación clínica, los modos de falla y la preparación para la implementación de la IA en la imagenología MEQ, centrándonos en dónde la IA puede mejorar la atención, dónde comúnmente no logra aportar valor y cómo debe estructurarse la validación antes de la adopción rutinaria. Las fortalezas reportadas se agrupan en casos de uso estrechos y sensibles al tiempo, como el triaje de fracturas y el apoyo al flujo de trabajo, mientras que las limitaciones surgen repetidamente de la pobre generalización, el encuadre estrecho de las tareas, el sesgo, las debilidades del estándar de referencia y el desajuste con el flujo de trabajo. Un mensaje central es que una alta precisión diagnóstica no equivale a valor clínico, el cual debe juzgarse por los efectos sobre el manejo, los desenlaces, la seguridad, la eficiencia y la equidad. Delineamos expectativas mínimas de validación, incluidas la validación externa, la evaluación prospectiva del impacto clínico y el monitoreo continuo de la deriva del rendimiento y el sesgo.

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Publicado

2026-03-25

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Artículo de revisión

Cómo citar

1.
Visconti-Lopez FJ, Lozada-Martínez ID. Validación clínica de la inteligencia artificial en imágenes musculoesqueléticas: una revisión narrativa estructurada de resultados, modos de fallo y preparación para la implementación. Med Educ Clin Pract [Internet]. 2026 Mar. 25 [cited 2026 Jun. 6];1(2):e017. Available from: https://revistas.cientifica.edu.pe/index.php/mecp/article/view/3187