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Narrative review demonstrates improved diagnostic accuracy from AI in maxillofacial surgery candidates, highlighting critical safety and regulatory gaps.
Facial and maxillofacial computed tomography angiography (CTA) has become an indispensable diagnostictool for orthognathic and reconstructive surgical planning, vascular malformation assessment, vascularizedtumors, and complex facial trauma. In parallel, artificial intelligence (AI) — particularly convolutional neuralnetworks and deep learning architectures — has been progressively integrated into each of these workflows,supporting automated bone and vessel segmentation, virtual surgical planning, fracture detection, and perforatormapping for microsurgical flap reconstruction. This narrative review synthesizes recent literature on thisintersection, with specific emphasis on patient safety: algorithmic bias and limited model generalizability,“black-box” opacity and lack of explainability, gaps in liability and regulatory oversight, and the ongoing needfor human supervision. We conclude that, although AI applied to maxillofacial CTA shows consistent gains inaccuracy and workflow efficiency, responsible clinical adoption depends on prospective multicenter validation,algorithmic transparency, and clearly defined accountability frameworks.
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Calazans et al. (2026) studied this question.
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