Machine Learning y Big Data en la gestión del riesgo en salud
Palabras clave:
Big Data; desafíos éticos; inteligencia artificial; Machine Learning; predicción de riesgosResumen
Introducción: La revolución tecnológica ha transformado profundamente la atención médica, al acumular grandes volúmenes de datos clínicos. Machine Learning y Big Data han surgido como herramientas clave para mejorar la gestión del riesgo y la personalización de tratamientos, al presentar, tanto oportunidades como desafíos en el ámbito de la salud.
Objetivo: Realizar una revisión bibliográfica sobre la utilización de Machine Learning y Big Data como herramientas clave para mejorar la gestión del riesgo y la personalización de tratamientos.
Métodos: Se realizó una revisión bibliográfica sistemática bajo las pautas de PRISMA, y la utilización de bases de datos académicas y científicas. Se seleccionaron estudios relevantes de los últimos 20 años en inglés y español, con enfoque en la aplicación de ciencia de datos e inteligencia artificial en la salud. La revisión se hizo en dos etapas: selección inicial por títulos y resúmenes, seguida por una lectura detallada de los textos completos.
Resultados: La revisión destacó avances significativos en áreas como el diagnóstico por imágenes y el análisis de datos de dispositivos portátiles, que han mejorado la precisión en la predicción de riesgos de salud. Sin embargo, se identificaron desafíos éticos y técnicos, como el sesgo en los algoritmos y expectativas desmesuradas sobre las capacidades de estas tecnologías.
Conclusiones: Machine Learning y Big Data tienen un gran potencial para transformar la atención médica, pero es crucial combinar estas tecnologías con la inteligencia humana para superar los desafíos éticos y técnicos. Esto garantizará una atención médica más personalizada, eficiente y equitativa.
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