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

Area of Science:

• Artificial intelligence in dentistry
• Computer vision in endodontics
• Generative AI applications

Background:

• AI, particularly discriminative deep learning, is established in endodontic diagnosis and treatment.
• Generative AI (GenAI) models, capable of creating new data, are emerging in this field.
• The current state and applications of GenAI in endodontics require thorough exploration.

Purpose of the Study:

• To review the current status of Generative AI (GenAI) models in endodontic computer vision and imaging.
• To explore potential applications of GenAI in endodontics.
• To evaluate the capabilities and future implications of GenAI in endodontic diagnosis, imaging, and treatment planning.

Main Methods:

• A comprehensive narrative review of literature was performed.
• Searches were conducted in major databases (PubMed/MEDLINE, Web of Science, Embase, Scopus, arXiv) up to July 2025.
• Keywords included generative models, dental imaging, and endodontics to examine GenAI principles and applications.

Main Results:

• GenAI facilitates synthetic dental image generation for training data augmentation and rare pathology education.
• Image enhancement techniques (super-resolution, denoising) improve diagnostic quality and detection rates for challenging anatomical structures.
• Applications include 2D-to-3D reconstruction, cross-modality image conversion (e.g., CBCT to MRI), and AI-driven treatment planning tools.

Conclusions:

• GenAI models offer significant potential for advancing endodontic treatments via data synthesis and image enhancement.
• Current research primarily focuses on technical feasibility, with limited clinical validation in real-world endodontic settings.
• Bridging the gap between technical development and clinical implementation is crucial for realizing GenAI's full potential in endodontics.