Modelos de inteligencia artificial para apoyo clínico en cirugía dental e implantología: una revisión sistemática
DOI:
https://doi.org/10.61347/ei.v5i1.278Palabras clave:
Aprendizaje automático, cirugía dental, implantología oral, inteligencia artificial, modelos predictivos, redes neuronales convolucionalesResumen
La inteligencia artificial (IA) se ha consolidado como una herramienta clave en la medicina contemporánea, destacándose por su capacidad para optimizar el diagnóstico, la planificación y la toma de decisiones clínicas mediante el análisis de grandes volúmenes de datos. En cirugía dental e implantología, su aplicación ha experimentado un crecimiento significativo, especialmente en el procesamiento de imágenes y la predicción de resultados terapéuticos. El objetivo de la presente revisión sistemática fue analizar de manera integral los modelos de IA utilizados como apoyo clínico en estas áreas, así como evaluar su desempeño diagnóstico e identificar sus principales limitaciones y desafíos de implementación. Se realizó una revisión sistemática siguiendo las directrices de la declaración PRISMA 2020, mediante una búsqueda estructurada en Scopus y PubMed, incluyendo estudios originales publicados entre 2020 y 2026. Tras el proceso de selección y evaluación del riesgo de sesgo, se incluyeron 19 estudios en la síntesis final. Los resultados evidencian un predominio de modelos de aprendizaje profundo (deep learning), particularmente redes neuronales convolucionales (CNN), así como modelos de detección de objetos (YOLO) y arquitecturas híbridas, aplicados principalmente al análisis de imágenes radiográficas en tareas como la segmentación anatómica, la clasificación de implantes, el diagnóstico radiográfico y la planificación implantológica. Estos modelos presentan un alto desempeño diagnóstico, con métricas superiores al 90% en múltiples tareas. No obstante, se identificaron limitaciones relacionadas con la calidad y disponibilidad de los datos, la generalización de los modelos y su validación en entornos clínicos reales. En conclusión, la IA representa una herramienta prometedora de apoyo clínico en implantología; sin embargo, su implementación efectiva requiere superar desafíos metodológicos, técnicos y regulatorios, así como fortalecer la evidencia mediante estudios clínicos robustos.
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Derechos de autor 2026 Adrián Sebastián Realpe Flores, Solange Daniela Chávez Jaramillo, Maritza Mishell Alarcón Mugmal, Carlos Elián Coque Bastidas
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
