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Prioridades de investigación en ciencia de datos e inteligencia artificial en salud global: un ejercicio de consenso
Peige Song1, Denan Jiang2, Jiali Zhou2
1School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
The Lancet. Global health
|February 19, 2026
Resumen
La inteligencia artificial (IA) y la ciencia de datos pueden mejorar la salud global, pero la investigación debe alinearse con las necesidades de los países de bajos y medianos ingresos (PBMI). Este estudio priorizó la investigación en IA para la preparación de epidemias, diagnósticos y equidad en salud en los PBMI.
Área de la Ciencia:
- Salud Global
- Ciencia de Datos
- Inteligencia Artificial (IA)
Sus antecedentes:
- Las aplicaciones de la ciencia de datos y la IA en salud global están creciendo pero fragmentadas.
- La investigación a menudo no se alinea con las necesidades de los países de bajos y medianos ingresos (PBMI).
Objetivo del estudio:
- Realizar un ejercicio global de establecimiento de prioridades de investigación para la IA y la ciencia de datos en salud global.
- Alinear la investigación con las necesidades y prioridades específicas de los PBMI.
Principales métodos:
- Se utilizó el método Child Health and Nutrition Research Initiative (CHNRI).
- Se involucró a 51 expertos globales para calificar 155 ideas de investigación según la viabilidad, el impacto y la equidad.
Principales resultados:
- Las principales prioridades incluyen la IA para la preparación de epidemias (predicción de brotes, diagnósticos, sistemas de alerta temprana).
- Otras áreas clave: asignación de recursos asistida por IA, telemedicina, salud móvil y manejo de enfermedades crónicas.
- Los expertos de países de ingresos bajos y medianos (PBMI) priorizaron las enfermedades infecciosas y la equidad en el diagnóstico; los expertos de países de altos ingresos se centraron en la infraestructura y el análisis del clima.
Conclusiones:
- El estudio proporciona una hoja de ruta para alinear la investigación en IA y ciencia de datos con las prioridades de salud global.
- Enfatiza la necesidad de agendas de investigación equitativas, que beneficien particularmente a los PBMI.
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