In recent years, the application of artificial intelligence (AI) in medicine has advanced rapidly, demonstrating remarkable potential particularly in the field of plastic surgery. As a major subdiscipline of plastic surgery, craniomaxillofacial surgery characterized by its heavy reliance on imaging, anatomical precision, and meticulous surgical techniques is especially amenable to the integration and transformation enabled by AI technologies. This review systematically summarized the current applications of AI in craniomaxillofacial surgery, including preoperative imaging analysis, diagnosis, and postoperative evaluation. Special emphasis was placed on the role of AI in the management of congenital craniofacial anomalies and facial contouring procedures. Additionally, the article discussed the existing challenges in technical implementation, clinical translation, and ethical governance. Finally, it explored the future potential of AI in multimodal data integration, high-throughput image annotation, and personalized treatment planning. AI is poised to propel craniomaxillofacial surgery toward a more precise, efficient, and intelligent era.

In recent years, the application of artificial intelligence (AI) in medicine has advanced rapidly, demonstrating remarkable potential particularly in the field of plastic surgery. As a major subdiscipline of plastic surgery, craniomaxillofacial surgery characterized by its heavy reliance on imaging, anatomical precision, and meticulous surgical techniques is especially amenable to the integration and transformation enabled by AI technologies. This review systematically summarized the current applications of AI in craniomaxillofacial surgery, including preoperative imaging analysis, diagnosis, and postoperative evaluation. Special emphasis was placed on the role of AI in the management of congenital craniofacial anomalies and facial contouring procedures. Additionally, the article discussed the existing challenges in technical implementation, clinical translation, and ethical governance. Finally, it explored the future potential of AI in multimodal data integration, high-throughput image annotation, and personalized treatment planning. AI is poised to propel craniomaxillofacial surgery toward a more precise, efficient, and intelligent era.

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal-occipital areas to the temporal-parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.