Applications of Artificial Intelligence in Dental Malocclusion: A Scoping Review of Recent Advances (2020–2025)

Introduction: Dental malocclusion affects more than half of the global population, causing significant functional and esthetic consequences. The integration of artificial intelligence (AI) into orthodontic care for malocclusion has the potential to enhance diagnostic accuracy, treatment planning, and clinical efficiency. However, existing research remains fragmented, and recent advances have not been comprehensively synthesized. This scoping review aimed to map the current landscape of AI applications in dental malocclusion from 2020 to 2025. Methods: The review followed the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. The authors conducted a systematic search across four databases (PubMed, Scopus, Web of Science, and IEEE Xplore) to identify original, peer-reviewed research applying AI to malocclusion diagnosis, classification, treatment planning, or monitoring. The review screened, selected, and extracted data using predefined criteria. Results: Ninety-five studies met the inclusion criteria. The majority employed convolutional neural networks and deep learning models, particularly for diagnosis and classification tasks. Accuracy rates frequently exceeded 90%, with robust performance in cephalometric landmark detection, skeletal classification, and 3D segmentation. Most studies focused on Angle’s classification, while anterior open bite, crossbite/asymmetry, and soft tissue modeling were comparatively underrepresented. Although model performance was generally high, study limitations included small sample sizes, lack of external validation, and limited demographic diversity. Conclusions: AI offers the potential to support and enhance the diagnosis and management of malocclusion. However, to ensure safe and effective clinical adoption, future research must include reproducible reporting, rigorous external validation across sites/devices, and evaluation in diverse populations and real-world clinical workflows.