Generative Artificial Intelligence and 3D Printing in Craniomaxillofacial Deformities: A Narrative Review of Emerging Synergies

Craniomaxillofacial deformities are primarily caused by congenital anomalies, trauma, or postoperative defects following tumor resection, involving both bone and soft tissues with complex anatomy and substantial individual variability. Traditional repair methods rely on manual design and modeling, which are inefficient, highly subjective, and fail to balance morphological compatibility with mechanical stability. In recent years, the rapid advancement of generative artificial intelligence and three-dimensional (3D) printing technologies has provided novel technical pathways for the precision reconstruction of craniomaxillofacial deformities. This review synthesizes the current applications of generative AI in bone defect reconstruction, personalized implant design, microstructure design of 3D-printed implants, and intelligent process control. It summarizes the advantages of existing technologies in improving design accuracy, shortening preparation cycles, and enhancing repair outcomes. Furthermore, this review analyzes the prevailing challenges, including insufficient data standardization, limited algorithm interpretability, and a lack of clinical validation, while also offering perspectives on future technological integration and clinical translation. The findings indicate that the convergence of generative AI and 3D printing holds promise for advancing craniomaxillofacial deformity repair from an experience-dependent approach toward precision and intelligent reconstruction, exhibiting favorable prospects for clinical application.