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Los grandes modelos de lenguaje codifican el conocimiento clínico

Karan Singhal1, Shekoofeh Azizi2, Tao Tu3

  • 1Google Research, Mountain View, CA, USA. karansinghal@google.com.

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|July 12, 2023
PubMed
Resumen
Este resumen es generado por máquina.

Los grandes modelos de lenguaje (LLM) son prometedores en medicina, pero requieren una evaluación rigurosa. Un nuevo punto de referencia, MultiMedQA, y las evaluaciones en humanos revelan las limitaciones actuales de LLM, destacando la necesidad de mejorar el desarrollo clínico de la IA.

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Área de la Ciencia:

  • Inteligencia artificial
  • La informática médica
  • Procesamiento del lenguaje natural

Sus antecedentes:

  • Los grandes modelos de lenguaje (LLM) demuestran capacidades avanzadas, pero se enfrentan a altos estándares para el uso clínico.
  • Las evaluaciones actuales de los conocimientos médicos en los LLM a menudo se basan en puntos de referencia automatizados limitados.

Objetivo del estudio:

  • Introducir MultiMedQA, un punto de referencia completo para la evaluación de las LLM en la respuesta a preguntas médicas.
  • Establecer un marco de evaluación humana que evalúe la factualidad, la comprensión, el razonamiento, el daño y el sesgo en las respuestas de LLM.
  • Evaluar el rendimiento del modelo de lenguaje de Pathways (PaLM) y del Flan-PaLM en el punto de referencia MultiMedQA.

Principales métodos:

  • Desarrolló MultiMedQA, integrando seis conjuntos de datos de control de calidad médica y el nuevo conjunto de datos HealthSearchQA.
  • Implementó un protocolo de evaluación humana para las respuestas médicas generadas por LLM.
  • Se evaluaron PaLM y Flan-PaLM utilizando varias estrategias de estimulación en MultiMedQA.
  • Se ha introducido la puesta a punto de instrucciones para la adaptación del dominio de los LLM.

Principales resultados:

  • Flan-PaLM logró una precisión de última generación en todos los conjuntos de datos de opción múltiple de MultiMedQA, incluido el 67,6% en MedQA (preguntas al estilo USMLE).
  • La evaluación humana identificó brechas significativas en el rendimiento de LLM a pesar de las fuertes puntuaciones automatizadas.
  • La puesta a punto de instrucciones condujo a Med-PaLM, que mostró un rendimiento mejorado, pero se mantuvo por debajo del nivel clínico.

Conclusiones:

  • El rendimiento del LLM en medicina mejora con la afinación rápida de la escala y la instrucción.
  • Los LLM actuales tienen limitaciones en las aplicaciones clínicas, lo que subraya la necesidad de marcos de evaluación sólidos.
  • El desarrollo posterior es crucial para crear LLM seguros y eficaces para la atención sanitaria.