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De los modelos de aprendizaje automático clásicos a los modelos fundacionales emergentes: Revisión de la integración
ArXiv
|December 25, 2025
Resumen
Los modelos fundacionales (FM) están revolucionando la investigación del cáncer al integrar diversos tipos de datos. Esta revisión describe el cambio del aprendizaje automático tradicional a los FM para la integración de datos multimodales en oncología, allanando el camino para descubrimientos impulsados por IA.
Sus antecedentes:
- La investigación del cáncer depende cada vez más de la integración de diversas modalidades de datos, como genómica, proteómica, imágenes y datos clínicos.
- La extracción de información procesable de estos conjuntos de datos complejos y heterogéneos presenta un desafío significativo.
- Los modelos fundacionales (FM) ofrecen vías prometedoras para el descubrimiento de biomarcadores, la mejora de diagnósticos y el tratamiento personalizado del cáncer.
Conclusiones:
- Los métodos integradores actuales que utilizan FM sientan las bases para los modelos de IA a gran escala de próxima generación en oncología.
- Esta revisión es la primera en trazar sistemáticamente la evolución del ML a los FM en la integración de datos multimodales del cáncer.
- Estos avances son fundamentales para el futuro de la investigación del cáncer impulsada por IA y la medicina personalizada.
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