From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial...

Area of Science:

• Dental imaging and artificial intelligence (AI)
• Medical informatics and AI validation
• Translational research in medical AI

Background:

• AI in dental imaging is rapidly advancing.
• Internal performance metrics do not guarantee clinical applicability.
• External validation and generalizability are crucial for AI adoption.

Purpose of the Study:

• To systematically review the translational progress of AI models in dental imaging.
• To evaluate AI models beyond internal development towards external validation and clinical readiness.
• To assess generalizability, reproducibility, privacy-preserving learning, and implementation readiness.

Main Methods:

• Systematic literature search of PubMed/MEDLINE, Scopus, and Embase up to May 2026.
• Inclusion of primary empirical studies on human dental/oral imaging data.
• Synthesis of evidence using structured narrative synthesis (Synthesis Without Meta-analysis framework).

Main Results:

• Fifteen studies (2023-2026) covered diverse dental AI applications.
• Evaluated AI models for external validation, generalizability, reproducibility, and privacy.
• Inconsistent reporting on reproducibility, explainability, and workflow integration.

Conclusions:

• Emerging translational progress in externally tested dental AI models, but development is uneven.
• Clinical implementation requires improved reproducibility, meaningful validation, and workflow assessment.
• Addressing regulatory, organizational, and human factors is essential for AI readiness.