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Colon-Pilot: una herramienta de IA generativa para recomendaciones de vigilancia de colonoscopia automatizada y

Sushil Kumar Garg¹, Brayden Mau², Jeffery Hubers¹

¹Division of Gastroenterology and Hepatology, Mayo Clinic Health System, Eau Claire, WI.

The American journal of gastroenterology

|February 10, 2026

Resumen

Una nueva herramienta de IA, Colon-Pilot, aplica con precisión las pautas de vigilancia de la colonoscopia y los puntos de referencia de calidad, superando a los endoscopistas humanos. Este sistema mejora la prevención del cáncer colorrectal al mejorar la eficiencia y la calidad.

Área de la Ciencia:

Gastroenterología y Gastroenterología.
La inteligencia artificial en la medicina.
Informática de la salud Informática de la salud.

Sus antecedentes:

La vigilancia precisa de la colonoscopia requiere el cumplimiento de las directrices (2020 USMSTF) y los puntos de referencia de calidad (2024 ACG / ASGE), que son desafiantes en la práctica.
Colon-Pilot, un sistema de apoyo a la decisión clínica (CDSS) impulsado por un gran modelo de lenguaje (LLM) que utiliza GPT-4o, se desarrolló para automatizar y estandarizar estas funciones.

Objetivo del estudio:

Desarrollar y validar Colon-Pilot para automatizar las recomendaciones de vigilancia de la colonoscopia y el benchmarking de calidad.
Evaluar la precisión del sistema en la aplicación de las directrices de USMSTF y en el cumplimiento de los indicadores de calidad ACG/ASGE.

Principales métodos:

Validación humana en el bucle: Las recomendaciones de vigilancia de Colon-Pilot para 596 colonoscopias se compararon con el consenso de expertos y las pautas USMSTF de 2020.
Auditoría administrativa automatizada: Colon-Pilot se aplicó a 42.632 colonoscopias para calcular 2024 indicadores de calidad ACG / ASGE, con recomendaciones generadas automáticamente sujetas a criterios de seguridad para revisión manual.

Principales resultados:

Colon-Pilot logró un 97,5% de precisión conforme a las directrices en casos automatizados, superando significativamente las recomendaciones endoscópicas originales (69,7%).
El sistema superó los objetivos de calidad de 2024, incluida la tasa de detección de adenoma (49,8%), la tasa de detección de lesiones serrilladas sesiales (17,7%), la adecuación de la preparación intestinal (91,8%) y la tasa de intubación cecal (97,4%).
Colon-Pilot recomendó intervalos de vigilancia apropiados con alta precisión, marcando solo el 12,4% de los casos para revisión manual.

Conclusiones:

Colon-Pilot demuestra una alta fidelidad en la aplicación de directrices de vigilancia y evaluación comparativa de calidad automatizada para colonoscopias.
El sistema impulsado por LLM supera a los endoscopistas no asistidos en la adhesión a las pautas, ofreciendo una solución escalable para mejorar la prevención del cáncer colorrectal.
Combinando protocolos de seguridad con informes automatizados, Colon-Pilot mejora tanto la eficiencia como la calidad en la práctica clínica.

Palabras clave:

Apoyo clínico a la toma de decisiones.Indicadores de calidad de la colonoscopia.Prevención del cáncer colorrectal y su prevención.Los grandes modelos de lenguaje.La vigilancia después de la polipectomía.

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