In this narrative review, the researchers examine the clinical implications and accuracy of AI applications used in restorative dentistry. Such topics of interest are caries detection and classification, detection of the finish line and margin in crown preparation, prediction of restoration failure, and linkage with CAD/CAM and prosthodontic workflows. The objective of the review is to summarize the available evidence, assess the validity of the diagnosis, and discuss whether AI can transform restorative practice. Recent research indicates that AI models are always highly diagnostic, and in some instances, they perform better than junior clinicians in caries recognition. It has also been demonstrated that AI-based systems can reliably detect restorative margins and predict the occurrence of restoration failure, which is beneficial in long-term treatment planning. Furthermore, in the field of prosthodontics, it has been suggested that the application will lead to higher efficiency and accuracy in implant-supported restorations and in the creation of crowns. AI has a huge potential to improve restorative dentistry as a complementary measure to improve clinician performance and patient care. To support successful translation to clinical applications, further validation using large-scale studies, integration into digital workflows, and resilient ethical models will be required.
