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La inteligencia artificial mejora la eficiencia de la detección en ensayos clínicos de enfermedad inflamatoria
Akshita Gupta1, Ryan C Ungaro1, Adam Steinlauf1
1Department of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Inflammatory bowel diseases
|February 18, 2026
Resumen
Una novedosa herramienta de IA que utiliza modelos de lenguaje grandes (LLM) mejoró el reclutamiento de ensayos clínicos de enfermedad inflamatoria intestinal (EII). La herramienta de IA mejoró la identificación de pacientes y la eficiencia de la detección en comparación con las referencias médicas tradicionales.
Área de la Ciencia:
- Reclutamiento de ensayos clínicos
- Inteligencia artificial en medicina
- Investigación de la enfermedad inflamatoria intestinal (EII)
Sus antecedentes:
- Los ensayos clínicos son cruciales para avanzar en el tratamiento de la EII.
- El reclutamiento de pacientes sigue siendo un cuello de botella importante en la finalización de los ensayos clínicos.
- Los métodos actuales para identificar pacientes elegibles pueden ser ineficientes.
Objetivo del estudio:
- Evaluar la eficacia de una herramienta de IA basada en modelos de lenguaje grandes (LLM) para mejorar la identificación de pacientes para ensayos clínicos de EII.
- Comparar el rendimiento de la herramienta de IA con los métodos tradicionales de referencia de médicos.
- Evaluar el impacto de la IA en la eficiencia de la detección de ensayos clínicos.
Principales métodos:
- Desarrollo e implementación de una herramienta de IA basada en LLM para examinar registros médicos electrónicos (EMR).
- La herramienta de IA se programó con criterios de inclusión y exclusión específicos para ensayos clínicos de EII.
- Se generaron listas de pacientes candidatos y se clasificaron mediante la herramienta de IA para su revisión.
Principales resultados:
- La herramienta de IA basada en LLM mejoró significativamente la identificación de pacientes potencialmente elegibles para ensayos clínicos de EII.
- La detección impulsada por IA demostró una mayor eficiencia en comparación con las referencias médicas estándar.
- Las listas de candidatos clasificados facilitaron un proceso de revisión más optimizado.
Conclusiones:
- Las herramientas de IA basadas en LLM ofrecen una solución prometedora para mejorar el reclutamiento de pacientes para ensayos clínicos de EII.
- La IA puede optimizar el proceso de detección, lo que lleva a una inscripción de ensayos más rápida y eficiente.
- Esta tecnología tiene el potencial de acelerar la investigación de la EII y el desarrollo terapéutico.
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