AI Promise Meets Reality: Why Orthodontists Aren't Using Smart Diagnostic Tools
A new review of 26 studies reveals that deep learning systems for orthodontics work well in labs but fail in real clinics—largely due to poor data quality, limited testing, and lack of standardization. The finding exposes a critical gap between AI research and clinical adoption that could delay patient benefits and waste development resources.
Originaltitel: Barriers to deep learning implementation in orthodontics: data, methodological, and translational challenges—a scoping review
<p>Background: Deep learning (DL) has attracted increasing attention in orthodontics, with many studies reporting high diagnostic and predictive accuracy. However, despite these promising results, routine clinical adoption remains limited.</p><p>Objectives: This scoping review aimed to explore the main barriers that restrict the translation of DL systems from research environments into everyday orthodontic practice.</p><p>Methodology: The review was conducted in accordance with PRISMA-ScR guidelines. Electronic searches were performed in PubMed, Scopus, Web of Science, and IEEE Xplore for English-language studies published between 2015 and June 2025. Studies reporting quantitative performance of DL-based orthodontic applications were included. Data were extracted using a standardized charting form and synthesized using a barrier-centered thematic approach.</p><p>Results: From 612 records, 26 studies were included. While most demonstrated high internal performance, consistent barriers to clinical implementation were identified. These primarily involved limited dataset diversity, single-center data sourcing, absence of external validation, and methodological heterogeneity. In addition, reliance on controlled research settings and insufficient real-world testing further restricted translation into routine orthodontic practice.</p><p>Conclusion: Although deep learning shows promising technical performance in orthodontics, meaningful clinical integration remains limited. Overcoming current barriers will require stronger validation standards, greater transparency, collaborative multicenter research, and implementation strategies that align with real clinical workflows.</p>