Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Integrating artificial intelligence into medical imaging offers potential for dental education. While existing models automate diagnosis, they often lack the interpretive depth needed for comprehensive student training. To address these limitations, this paper presents Gen-Mentor , a human-in-the-loop instructional framework that integrates the DentDiff-VLM backbone into a dental-radiography workflow. The backbone uses Faster R-CNN to localize four target radiographic findings: Filling, Implant, Impacted Tooth, and Cavity. A conditional diffusion model supports curriculum expansion by generating class-specific synthetic ROI candidates as candidate instructional assets. A vision–language...

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