Top 1 Most Influential Orthodontic Articles on PubMed

Reference

Nordblom NF, Büttner M, Schwendicke F.
Artificial Intelligence in Orthodontics: Critical Review.
Journal of Dental Research. 2024;103(6):577-584.
DOI: 10.1177/00220345241235606
PMID: 38682436

Background

Artificial intelligence (AI) has rapidly become one of the most transformative technologies in orthodontics. Combined with digital workflows, intraoral scanners, cone-beam computed tomography (CBCT), and three-dimensional treatment planning, AI has the potential to improve diagnostic accuracy, automate repetitive tasks, and support clinical decision-making.

Despite the growing number of AI applications, questions remain regarding their clinical reliability, generalizability, and integration into everyday orthodontic practice.

Objective

This critical review aimed to evaluate the current applications of artificial intelligence in orthodontics, identify the most promising clinical uses, and discuss the challenges preventing widespread clinical implementation.

Materials and Methods

The authors critically reviewed recent studies investigating machine learning and deep learning applications in orthodontics. The review focused on AI performance in diagnosis, landmark detection, treatment planning, growth prediction, and clinical workflow automation.

Results

The review demonstrated that AI performs particularly well in image analysis tasks, especially automated cephalometric landmark identification and digital image segmentation.

Machine learning models also showed encouraging results in treatment planning, including the prediction of extraction requirements, orthognathic surgery indications, and assessment of treatment outcomes.

However, most published models were developed using relatively small, single-center datasets, limiting their generalizability to broader patient populations.

Main Scientific Findings

• AI significantly reduces the time required for cephalometric analysis.
• Automated landmark detection now approaches expert-level accuracy in many studies.
• AI improves consistency by reducing operator variability.
• Decision-support systems may assist orthodontists during diagnosis and treatment planning.
• Human supervision remains essential for every clinical decision.
• Large multicenter datasets are still needed before routine clinical adoption.

Authors' Conclusion

Artificial intelligence is expected to become an integral component of digital orthodontics. Rather than replacing orthodontists, AI should be viewed as a clinical decision-support technology capable of improving efficiency, standardization, and diagnostic precision.

Future research should prioritize external validation, standardized datasets, and real-world clinical evaluation before widespread implementation.

Clinical Relevance for Orthodontists

AI is already transforming orthodontic workflows through automated cephalometric analysis, digital model evaluation, treatment simulations, and patient monitoring. Nevertheless, clinical expertise remains indispensable for interpreting AI-generated recommendations and making individualized treatment decisions.

Key Takeaways

• ✔ AI enhances diagnostic efficiency and reproducibility.
• ✔ Landmark detection is currently the most mature AI application.
• ✔ Treatment planning support is improving rapidly.
• ✔ AI complements—but does not replace—the orthodontist.
• ✔ Future progress depends on high-quality multicenter clinical datasets.

Keywords

Artificial Intelligence; Machine Learning; Orthodontics; Digital Orthodontics; Cephalometric Analysis; Deep Learning; Treatment Planning.