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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Aprovechamiento de la IA generativa para la toma de decisiones clínicas interpretables a través de gráficos causales

Mehmet Eren Ahsen1, Rand Kittani2, Travis Gerke3

  • 1Gies College of Business.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
Resumen

La IA generativa crea modelos causales estructurales (SCM) interpretables para la IA clínica, mejorando la inferencia causal. Estos SCM impulsados por IA muestran un rendimiento comparable al de los expertos humanos en la estimación de los efectos del tratamiento del COVID-19.

Palabras clave:
IA generativamodelos causales estructuralesIA clínicainferencia causaltoma de decisiones clínicasCOVID-19

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Área de la Ciencia:

  • Inteligencia artificial en medicina
  • Inferencia causal
  • Informática de la salud

Sus antecedentes:

  • La adopción de la IA clínica se ve obstaculizada por la falta de interpretabilidad.
  • La IA generativa ofrece potencial para la integración del conocimiento médico.
  • Los modelos causales estructurales (SCM) son cruciales para la inferencia causal fiable.

Objetivo del estudio:

  • Desarrollar un marco computacional que utilice IA generativa para crear SCM interpretables para aplicaciones clínicas.
  • Mejorar el soporte de decisiones clínicas, la mejora de la calidad y la gestión de la salud de la población.
  • Reducir la brecha de interpretabilidad en la IA clínica para la medicina basada en evidencia.

Principales métodos:

  • Un estudio de caso utilizando el conjunto de datos del Midwest Healthcare Conference Causal Diagram Challenge.
  • Comparación de modelos de lenguaje grandes (LLM) basados en transformadores con el rendimiento humano.
  • Emulación de ensayos dirigidos para estimar los efectos del tratamiento del COVID-19 sobre la mortalidad utilizando SCM.
  • Comparación con los resultados publicados de ensayos controlados aleatorizados (ensayo RECOVERY).

Principales resultados:

  • Los SCM diseñados por IA lograron una cobertura de bootstrap >90% para la mayoría de los estratos de gravedad de pacientes con COVID-19.
  • Tanto los modelos de IA como los humanos mostraron una plausibilidad clínica equivalente y un rendimiento estadístico similar.
  • Los enfoques basados en SCM demostraron una cobertura significativamente mayor (76-98%) que los métodos tradicionales (1-37%).

Conclusiones:

  • Los SCM interpretables generados por IA pueden facilitar la inferencia causal fiable en entornos clínicos.
  • El marco permite una colaboración significativa entre humanos y IA manteniendo el rigor metodológico.
  • Los SCM son una solución prometedora para mejorar la adopción y la confiabilidad de la IA clínica.