Generative Artificial Intelligence for Computer Vision in Endodontics: A Review of Current State and Future Potential

BACKGROUND: Computer vision methods based on artificial intelligence (AI) have found numerous applications in endodontic diagnosis and treatment planning. While most current applications employ discriminative deep learning models for detection and classification tasks, the field is now witnessing the rise of generative AI (GenAI), a class of AI models that learn to generate new data samples resembling the original training data. Despite the potential of GenAI, its current state, applications in endodontics and challenges have not been thoroughly explored. OBJECTIVES: This narrative review aims to explore the current state and potential applications of GenAI models in computer vision and imaging within endodontics. METHODS: A literature search was conducted in PubMed/MEDLINE, Web of Science, Embase, Scopus and arXiv through July 2025, using predefined keywords related to generative models, dental imaging and endodontics. This comprehensive narrative review examines the fundamental principles and current applications of vision GenAI models in endodontics, aiming to clarify their capabilities and evaluate their prospective implications for future applications in endodontic diagnosis, image enhancement and treatment planning. RESULTS: Diverse applications of GenAI in endodontics were identified. Synthetic data generation models can produce unconditional and conditional dental images to augment training datasets and create educational content for rare pathologies. Image enhancement techniques, including super-resolution, denoising and artefact reduction, show promise in improving diagnostic quality by revealing fine anatomical details. Studies report improvements in quantitative metrics and clinical detection rates, particularly for challenging structures such as second mesiobuccal canals in maxillary molars. Modality conversion applications include 2D-to-3D reconstruction from radiographs, cone-beam computed tomography (CBCT) to magnetic resonance imaging conversion for enhanced soft tissue visualisation, and CBCT to micro-computed tomography transformation for improved spatial resolution. Treatment enhancement applications encompass customised surgical guide design, treatment outcome prediction and AI-based computer-aided design for restoration of endodontically treated teeth. However, current research is predominantly found in other fields of dentistry and emphasises technical feasibility rather than clinical validation in real-world scenarios. CONCLUSION: GenAI models are promising tools for enhancing endodontic treatments through data synthesis, image quality improvement and treatment optimisation. However, there are significant gaps between technical demonstrations and clinical implementation.