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IA generativa para el desarrollo de modelos fundacionales en radiología e imagen: perspectivas de ingeniería

June-Goo Lee1, Sunggu Kyung1, Namkug Kim1

  • 1Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.

Biomedical engineering letters
|January 26, 2026
PubMed
Resumen

La IA generativa es crucial para avanzar en los modelos fundacionales médicos en radiología al permitir el aprendizaje autosupervisado y la generación de datos sintéticos. Estos modelos de IA abordan desafíos clave, allanando el camino para infraestructuras de IA médicas escalables y adaptables.

Palabras clave:
inteligencia artificial generativamodelos fundacionalesradiologíaimagen médicaaprendizaje autosupervisadodatos sintéticosmodelos multimodalesgrandes modelos de lenguajeinfraestructura de IA

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Área de la Ciencia:

  • Inteligencia artificial
  • Imagen médica
  • Modelos fundacionales

Sus antecedentes:

  • Los datos anotados en radiología son limitados y heterogéneos.
  • La IA generativa ofrece soluciones para el aprendizaje autosupervisado y la generación de datos sintéticos.
  • La IA generativa aborda los desafíos de escalabilidad, alineación multimodal y diversidad de datos en la IA médica.

Objetivo del estudio:

  • Revisar el papel de la IA generativa en los modelos fundacionales médicos.
  • Explorar los marcos de modelos generativos y las técnicas de aprendizaje de representación.
  • Describir los modelos multimodales de lenguaje grande (MLLM) para aplicaciones clínicas.

Principales métodos:

  • Revisión de modelos generativos (VAE, difusión, marcos autorregresivos).
  • Exploración de diseños híbridos y aprendizaje de representación (autoencoding enmascarado, aprendizaje contrastivo).
  • Descripción del diseño y entrenamiento de MLLM para integrar datos visuales, textuales y clínicos.

Principales resultados:

  • Los modelos de IA generativa forman la columna vertebral de los modelos fundacionales médicos.
  • Los diseños híbridos y el aprendizaje de representación mejoran el rendimiento del modelo.
  • Los MLLM integran datos diversos para aplicaciones como la generación de informes y el razonamiento clínico.

Conclusiones:

  • La IA generativa permite una IA médica escalable, adaptable y consciente de la privacidad.
  • Los estudios de caso demuestran la aplicación práctica de estos modelos.
  • Las direcciones futuras incluyen abordar los desafíos de alucinación, generalización y regulatorios para la implementación de IA confiable.