FROM DIAGNOSTIC AUTOMATION TO TRUSTWORTHY CLINICAL INTELLIGENCE: A SYSTEMATIC SCOPING REVIEW AND EVIDENCE-TO-DEPLOYMENT ROADMAP FOR ARTIFICIAL INTELLIGENCE IN DIGITAL DENTISTRY

Digital dentistry increasingly reports high-performing computational models, yet a central knowledge gap remains: existing reviews often catalogue technologies or deployment concerns without a common framework that distinguishes analytical performance from transportability, clinical validity, clinical utility, and lifecycle safety. Consequently, it remains unclear what level of real-world use is actually justified by a reported benchmark result. This systematic scoping review critically appraised literature available through July 2026; 147 records were screened and 56 studies were included for evidence synthesis. Rather than pooling incomparable accuracy values, the review evaluated data provenance, partitioning and leakage, reference standards, external validation, calibration and uncertainty, fairness, human factors, cybersecurity, prospective impact, and lifecycle monitoring across imaging, natural-language processing, multimodal and foundation models, robotics, AR/VR, tele-operation, synthetic data, and digital twins. The synthesis identifies an accuracy-to-deployment gap: evidence is comparatively mature for selected two-dimensional imaging and segmentation tasks, whereas multimodal assistants, generative systems, robotics, tele-operation, and digital twins remain limited by external, prospective, human-factor, or lifecycle evidence. The novelty of this review is an integrated evidence-to-deployment taxonomy, an M0-M5 clinical utility ladder, minimum reporting requirements, and staged evidence gates. The new knowledge created is that evidence maturity, not nominal accuracy alone, determines the defensible level of clinical use, and that the validation burden increases as autonomy, multimodality, generative behaviour, and post-deployment change increase. These outputs provide a practical basis for designing, reviewing, and governing clinically credible digital-dentistry research.