Cleft lip and cleft palate are common craniofacial abnormalities, causing significant functional, esthetic, and psychosocial issues if not treated early. Artificial intelligence (AI) is increasingly explored in cleft lip and/or palate (CL/P); however, the quality, consistency, and clinical readiness of the available evidence remain unclear. This study systematically reviewed the existing literature on AI applications in CL/P care and performed an exploratory meta-analysis.
Design
A comprehensive search was performed on PubMed, WOS, Scopus, and IEEE Xplore until November 2025, following the PECO question: “In patients with CL/P (P), how do AI approaches (E), compared with conventional diagnostic methods or human intelligence (C), perform in terms of diagnostic accuracy, predictive performance, or treatment outcomes (O)?”. Studies applying AI to human CL/P data for diagnostic, predictive, or treatment purposes were included. Risk of bias was assessed using appropriate checklists (eg, QUADAS-2). Due to study heterogeneity, random-effects meta-analyses were limited to subgroups evaluating CL/P detection on panoramic radiographs and prediction of orthognathic surgery using lateral cephalograms.
Results
The search identified 548 articles; after screening and full-text review, 52 studies were included. These studies addressed diagnosis, prediction, and treatment planning. Meta-analysis of CL/P detection indicated pooled sensitivity, specificity, and accuracy of 87%, 89%, and 90%. Prediction of orthognathic surgery showed sensitivity and specificity of 87% and 86%.
Conclusion
AI is increasingly applied in CL/P management, suggesting high accuracy and consistency in diagnosis, prediction, and treatment evaluation, often approaching expert-level performance.
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