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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

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Deep Learning in Dental Imaging: Advances, Challenges, and Future.

Emre Aydin¹, Zuhal Can²

¹Faculty of Engineering and Architecture, Computer Engineering, Eskişehir Osmangazi University, Eskişehir, Turkey.

|June 19, 2026

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Artificial intelligence (AI) enhances dental imaging analysis for tasks like disease detection and tooth identification. However, AI performs best with clear pathology, facing challenges with early disease and varied data, limiting its current clinical use to decision support.

Area of Science:

Dentistry and Oral Health
Medical Imaging Analysis
Artificial Intelligence in Healthcare

Background:

Artificial intelligence (AI) and deep learning (DL) are transforming dental radiography analysis.
Automated detection, classification, segmentation, and tooth identification are key applications.
This review synthesizes AI performance in dental imaging for practical clinical application.

Purpose of the Study:

To provide a practice-oriented synthesis of AI in dental imaging.
To link clinical questions and output types to reported AI performance.
To assess the implications of AI in routine dental care.

Main Methods:

A structured, semi-systematic literature search was conducted.
Quantitative eligibility criteria were applied to select studies.

Keywords:

Artificial intelligence in dentistry Automated diagnosis Deep learning Dental imaging

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Findings were synthesized across major dental imaging application areas.

Main Results:

AI demonstrates stronger performance for clearly visible pathology and well-defined boundaries.
Failure modes include early-stage disease, small lesions, overlapping anatomy, and artifacts.
Cross-study comparisons are often unreliable due to methodological and reporting inconsistencies.

Conclusions:

Near-term clinical roles for AI include calibrated decision support for case prioritization and clinician verification.
Future progress requires multi-center validation, severity-aware reporting, and uncertainty handling.
Auditable spatial outputs are crucial for integrating AI into clinical workflows.