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Modelos de base para la inteligencia artificial médica generalista

Michael Moor1, Oishi Banerjee2, Zahra Shakeri Hossein Abad3

  • 1Department of Computer Science, Stanford University, Stanford, CA, USA.

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|April 12, 2023
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Resumen

Introducimos modelos de inteligencia artificial médica generalistas capaces de realizar diversas tareas médicas con un mínimo de datos. Estos sistemas de IA flexibles interpretan varios tipos de datos para proporcionar razonamiento y explicaciones médicas avanzadas.

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Área de la Ciencia:

  • La inteligencia artificial médica
  • El aprendizaje automático en el cuidado de la salud

Sus antecedentes:

  • Los rápidos avances en inteligencia artificial (IA) están permitiendo nuevas aplicaciones médicas.
  • Los modelos actuales de IA a menudo requieren extensos datos etiquetados específicos de la tarea.

Objetivo del estudio:

  • Proponer un nuevo paradigma: IA médica generalista (IAG).
  • Describir las capacidades y los requisitos de los modelos GMAI.

Principales métodos:

  • Desarrollar el aprendizaje auto-supervisado en conjuntos de datos grandes y diversos.
  • Permitir una interpretación flexible de los datos médicos multimodales (imágenes, DSE, genómica, texto, etc.) En el caso de los

Principales resultados:

  • Los modelos GMAI pueden realizar diversas tareas con pocos o ningún dato específico de la tarea.
  • GMAI puede producir salidas expresivas como explicaciones de texto libre y anotaciones de imágenes.
  • Identificación de aplicaciones de alto impacto y capacidades técnicas necesarias para el GMAI.

Conclusiones:

  • GMAI representa un cambio significativo en las capacidades de IA médica.
  • GMAI requerirá nuevos enfoques para la regulación de la IA, la validación y las prácticas de recopilación de datos.