Beyond the Cleft: Can Artificial Intelligence Redefine the Future of Cleft Care

Cleft lip and palate (CLP) represent a major group of congenital craniofacial conditions that require coordinated, long-term care involving multiple healthcare specialties. The increasing adoption of artificial intelligence (AI) in dentistry and orthodontics has opened new avenues for addressing the diagnostic and therapeutic complexities associated with cleft management. This narrative review examines the evolving applications of AI throughout the CLP treatment pathway, highlighting its potential contributions to clinical assessment, treatment planning, and outcome evaluation. The review considers seven key areas in which AI-based technologies may support cleft care. These include the identification of cleft anomalies from prenatal ultrasound and photographic images using deep learning algorithms; computer-assisted planning and digital fabrication of nasoalveolar molding appliances; automated localization of cephalometric landmarks in patients with atypical craniofacial structures; three-dimensional assessment and segmentation of alveolar bone grafts using conebeam computed tomography (CBCT); radiographic identification of dental abnormalities frequently associated with CLP; predictive modelling of orthognathic surgical requirements and postoperative facial soft-tissue changes; and computerized analysis of speech patterns associated with velopharyngeal dysfunction. Research in these areas suggests that AI may improve the efficiency and consistency of diagnostic procedures, facilitate quantitative analysis, and assist clinicians in complex treatment decisions. However, the translation of these technologies into routine clinical practice remains challenging. Important barriers include limited sample sizes, inadequate representation of patient populations, insufficient validation across independent clinical settings, the possibility of algorithmic bias, and ethical issues related to patient data protection and professional accountability. For orthodontists involved in cleft care, AI offers opportunities to enhance clinical decision-making and streamline selected aspects of treatment. Nevertheless, its outputs must be interpreted in conjunction with comprehensive clinical examination, patient-specific findings, and multidisciplinary expertise. Future research should prioritize larger and more diverse datasets, independent clinical validation, transparent reporting, and evaluation of real-world patient outcomes to establish the reliability and clinical value of AI-assisted approaches in CLP management.