Impacto de la IA en la Predicción y Detección Oportuna de Enfermedades: Una Revisión Sistemática

Autores/as

  • Carlos Zepeda Lugo Universidad Autónoma de Baja California image/svg+xml
  • Andrea Insfran-Rivarola Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial
  • Ana Arévalos Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial

DOI:

https://doi.org/10.69681/lajae.v7i1.35

Palabras clave:

Machine learning, diagnóstico médico, algoritmos predictivos, salud 4.0

Resumen

La Inteligencia Artificial (IA) ha surgido como una herramienta transformadora en el entorno de la salud. Esta revisión tiene como objetivo evaluar los efectos de las tecnologías con base en IA en la predicción y detección oportuna de enfermedades. Se realizó una revisión sistemática de la literatura con base en los lineamientos de la metodología PRISMA. A partir de un total de 27,359 estudios identificados, 28 fueron incluidos debido a que demostraron la efectividad de los algoritmos de machine learning. Los resultados revelaron que los algoritmos se aplicaron principalmente para predecir enfermedades cardiovasculares (n = 9), neurológicas (n = 6), oncológicas (n = 5), hepáticas (n = 3), pulmonares (n = 3) e infecciosas (n = 2). Además, se midió la precisión de los principales algoritmos en 23 estudios y 19 reportaron valores superiores al 90%. La aplicación de técnicas de IA, ha demostrado un potencial significativo para mejorar el diagnóstico y la predicción de una amplia gama de enfermedades.

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Biografía del autor/a

Carlos Zepeda Lugo, Universidad Autónoma de Baja California

Recibió el título de Ingeniero en Bioingeniería por la Universidad Autónoma de Baja California (UABC), México, en 2012. En 2016 y 2020 obtuvo el grado de Maestro en Ciencias en Ingeniería Industrial y el grado de Doctor en Ciencias por la UABC, respectivamente. Actualmente es profesor de ingeniería industrial en la Facultad de Ingeniería, Arquitectura y Diseño de la Universidad Autónoma de Baja California, México. Está a cargo del área de Ingeniería Biomédica del Instituto de Servicios de Salud del Estado de Baja California. Su investigación se centra en Lean-Six Sigma en el ámbito de la salud, la ingeniería clínica y Healthcare 4.0. El Dr. Zepeda ha participado en diversos proyectos de investigación relacionados con la mejora de procesos en Salud.

Andrea Insfran-Rivarola, Laboratorio de Producción y Métodos del Departamento de Ingeniería Industrial

Doctora en Ciencias de la Ingeniería Industrial por la Universidad Autónoma de Baja California, México. Es docente investigadora del Departamento de Ingeniería Industrial y Jefa del Laboratorio de Producción y Métodos de la FIUNA. Sus líneas de investigación se centran principalmente en sistemas de gestión y mejora continua mediante Lean Six Sigma e inocuidad alimentaria. Además, se encuentra categorizada en el SISNI de Conacyt Paraguay en el nivel I. Es docente de las cátedras Lean Production y Análisis, Medición y Mejora de Procesos.

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Publicado

2024-12-31

Cómo citar

Zepeda Lugo, C. A., Insfran Rivarola, A. M., & Arévalos Ferreira, A. P. (2024). Impacto de la IA en la Predicción y Detección Oportuna de Enfermedades: Una Revisión Sistemática. Latin American Journal of Applied Engineering, 7(1), 25–35. https://doi.org/10.69681/lajae.v7i1.35

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