Inteligencia artificial en ortodoncia: un análisis bibliométrico

Autores/as

DOI:

https://doi.org/10.61347/ei.v4i1.108

Palabras clave:

Bibliometría, inteligencia artificial, ortodoncia, software Bibliometrix

Resumen

En los últimos años, la inteligencia artificial (IA) ha transformado significativamente el campo de la odontología y, en ortodoncia, las posibles aplicaciones de IA pueden ser revolucionarias. En este contexto, el presente estudio realizó un análisis bibliométrico para evaluar el estado actual de la investigación en IA aplicada a la ortodoncia, identificando tendencias emergentes, trabajos influyentes y perspectivas futuras. Se empleó una estrategia de búsqueda en la base de datos Scopus, seleccionando artículos que abordan el uso de IA en ortodoncia. Mediante el software Bibliometrix se examinaron 1293 estudios, analizando la evolución de la producción científica, los autores más influyentes, las revistas de mayor impacto y las palabras clave predominantes. Los resultados evidenciaron un aumento significativo en las publicaciones desde 2018, alcanzando su punto máximo en 2024. Las investigaciones se centraron en el diagnóstico automatizado, la planificación del tratamiento y el desarrollo de modelos predictivos. Estudios claves han demostrado el potencial de las redes neuronales en la segmentación de imágenes dentales y el análisis cefalométrico. China, Estados Unidos y Corea del Sur lideran la producción científica, mientras que la Universidad de Sichuan destaca como la institución más prolífica. Las perspectivas futuras sugieren una mayor integración de IA en la personalización de tratamientos y la automatización de procesos clínicos, con énfasis en modelos avanzados de aprendizaje profundo. Este estudio proporciona una visión estructurada de la evolución y el impacto de la IA en la ortodoncia, y puede servir como base para futuras investigaciones.

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2025-03-17

Cómo citar

González Muñoz, M. A., & González Muñoz, F. A. (2025). Inteligencia artificial en ortodoncia: un análisis bibliométrico. Esprint Investigación, 4(1), 243–261. https://doi.org/10.61347/ei.v4i1.108