Related Experiment Video
Updated: Jul 16, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial
Carlos M Ardila1,2, Anny M Vivares-Builes2,3, Eliana Pineda-Vélez2,3
1Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
Background/Objectives:
Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artificial intelligence models have progressed beyond internal algorithmic development toward external validation, generalizability, reproducibility, privacy-preserving learning, and clinical implementation readiness.
Methods:
Searches were conducted in PubMed/MEDLINE, Scopus, and Embase up to May 2026. Eligible studies were primary empirical investigations based on human dental or oral imaging data that assessed at least one translational-validation dimension beyond internal development, including external testing, multicenter or multi-device validation, cross-dataset reproducibility, or privacy-preserving learning. Evidence was synthesized using a structured narrative synthesis reported according to the Synthesis Without Meta-analysis framework.
Results:
Fifteen studies published between 2023 and 2026 were included. They addressed caries detection, periodontal bone loss, gingival inflammation, root morphology, palatal radicular grooves, radiographic quality control, tooth-width estimation, and dental-structure segmentation. Translational-readiness domains included external validation, generalizability, reproducibility, privacy-preserving learning, transparency, and workflow relevance. Validation varied across cohorts, repositories, centers, devices, cross-dataset benchmarks, and federated-learning settings. Reproducibility, annotation harmonization, uncertainty reporting, explainability, workflow evaluation, and code or model availability were inconsistent. Quantitative pooling was not performed because tasks, modalities, units of analysis, reference standards, validation designs, and metrics were highly heterogeneous.
Conclusions:
Within this selected subset of externally tested studies, translational progress is emerging but remains uneven. Implementation readiness requires stronger reproducibility, clinically meaningful validation, workflow evaluation, and attention to regulatory, organizational, and human-factor barriers.
