Inteligencia artificial en anestesiología: Modelos predictivos para mejorar la seguridad perioperatoria
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Introducción: La inteligencia artificial (IA) ha emergido como una herramienta transformadora en múltiples áreas de la medicina, particularmente en anestesiología. Los modelos predictivos desarrollados mediante técnicas de aprendizaje automático y aprendizaje profundo permiten analizar grandes volúmenes de datos clínicos en tiempo real, facilitando la anticipación de eventos adversos y mejorando la seguridad del paciente. Objetivo: Analizar la evidencia científica disponible sobre la aplicación de modelos predictivos basados en IA en anestesiología, con énfasis en su impacto en la seguridad perioperatoria. Metodología: Se realizó una revisión de la literatura en bases de datos biomédicas relevantes, incluyendo estudios publicados entre 2018 y 2025. La búsqueda bibliográfica fue actualizada por última vez en enero de 2026. Resultados: La IA ha demostrado una alta capacidad predictiva en eventos críticos como la hipotensión intraoperatoria, complicaciones respiratorias, mortalidad postoperatoria y delirio. Asimismo, su aplicación en anestesia personalizada permite optimizar la administración de fármacos anestésicos. Sin embargo, persisten desafíos relacionados con la calidad de los datos, la interpretabilidad de los modelos y aspectos éticos y legales. Conclusión: La IA representa una herramienta prometedora para mejorar la seguridad perioperatoria, aunque su implementación requiere validación clínica rigurosa y un enfoque ético adecuado.
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