Transformación de la epidemiología en la era de la inteligencia artificial
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Palabras clave
Epidemiología, Inteligencia Artificial, Medicina de Precisión, Toma de Decisiones, Sistemas de Salud
Resumen
La epidemiología ha sido fundamental para el análisis de los problemas de salud y para la toma de decisiones en los sistemas sanitarios y la salud pública. No obstante, los métodos tradicionales de la epidemiología, diseñados fundamentalmente para identificar asociaciones causales a nivel poblacional mediante medidas de datos agrupados, presentan limitaciones inherentes para capturar la heterogeneidad individual en la respuesta ante exposiciones específicas. Esta aproximación poblacional dificulta la predicción personalizada del desenlace en un individuo particular, cuyos factores de riesgo pueden manifestarse de forma distinta al promedio grupal, particularmente cuando intervienen múltiples variables contextuales y perfiles biológicos únicos.
Los avances en inteligencia artificial han generado herramientas capaces de integrar grandes volúmenes de información, identificar patrones complejos en subgrupos específicos y elaborar estimaciones más personalizadas, transicionando desde un enfoque reactivo basado en promedios poblacionales hacia modelos predictivos centrados en trayectorias individuales. Sin embargo, estos desarrollos no sustituyen los fundamentos metodológicos de la epidemiología, ya que la identificación de exposiciones, desenlaces y relaciones causales continúa dependiendo del marco conceptual epidemiológico.
Bajo esta perspectiva, las tensiones actuales no representan una crisis disciplinaria, sino una transición hacia enfoques más amplios que combinan análisis poblacionales con herramientas predictivas avanzadas. Esta integración resulta especialmente relevante para instituciones de gran escala, como las de seguridad social, que requieren modelos capaces de aprovechar datos diversos para mejorar la comprensión de los procesos de salud y apoyar decisiones clínicas y operativas.
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