Integrating Artificial Intelligence Into Orthodontic Education and Practice

Abstract

Integrating Artificial Intelligence Into Orthodontic Education and PracticeDear Editor,-Orthodontic treatment aims to correct dental malocclusion and improve oral health and aesthetics, but adverse effects like gingival recession and tooth mobility can occur.Predicting these issues is crucial for optimizing patient care.The integration of advanced AI technologies into orthodontic education is crucial, as highlighted by the study on Dental Sleep Medicine Education in Thailand, which underscores the need for comprehensive educational frameworks. 1 Similar to DSME, AI-based approaches in orthodontic curricula could enhance clinical competencies.AI-based multi-modal transformers can improve predictions of gingival recession and tooth mobility by integrating patient-specific factors and treatment outcomes. [2][3]3][4][5] These models analyse diverse data types, employing self-attention mechanisms to capture complex relationships within and between modalities, resulting in more precise predictions than traditional methods.For instance, a study on severe periodontitis used a mixed-effects logistic regression model, achieving high accuracy with an AUC of 0.983.Similarly, machine learning models like Random Forest have been used for orthodontic diagnosis and treatment planning with high accuracy. 3Another study developed a model to predict orthodontic treatment duration using pre-treatment variables, with significant predictors including extraction decisions and intermaxillary relationships. 4 Despite their potential, integrating transformer models into clinical workflows presents challenges.Data quality, computational power and generalizability across patient populations are critical issues. 3,4Additionally, integrating these models with clinical software requires ensuring interoperability and designing user-friendly interfaces for clinicians. 2,5thical and legal considerations also warrant attention to prevent biases in predictions.Collaboration among data scientists, clinicians and regulatory bodies is essential to address these challenges.In conclusion, AI-based multi-modal transformers show great potential in predicting gingival recession and tooth mobility during orthodontic treatment.Further research and integration into educational programs could revolutionize the field, enhancing precision and effectiveness in predicting adverse outcomes in high-risk individuals.

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