OMNI-Dent: Towards an Accessible and Explainable AI Framework for Automated Dental Diagnosis
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Authors
Jang, Leeje
Chiang, Yao-Yi
Hastings, Angela M
Pungchanchaikul, Patimaporn
Lucas, Martha B
Schultz, Emily C
Louie, Jeffrey P
Estai, Mohamed
Wang, Wen-Chen
Ip, Ryan HL
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Conference Contribution
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Computer Vision Foundation
Abstract
Accurate dental diagnosis is essential for oral healthcare, yet many individuals lack access to timely professional evaluation. Existing AI-based methods primarily treat diagnosis as a visual pattern recognition task and do not reflect the structured clinical reasoning used by dental professionals. These approaches also require large amounts of expert-annotated data and often struggle to generalize across diverse real-world imaging conditions. In this paper, we present OMNI-Dent, a data-efficient and explainable diagnostic framework that incorporates clinical reasoning principles into a Vision-Language Model (VLM)-based pipeline. The framework operates on multi-view smartphone photographs, embeds diagnostic heuristics from dental experts, and guides a general-purpose VLM to perform tooth-level evaluation without dental-specific fine-tuning of the VLM. By leveraging the VLM's existing visual-linguistic capabilities, OMNI-Dent supports diagnostic assessment in settings where curated clinical imaging is unavailable. We design OMNI-Dent as an early-stage assistive tool to help users identify potential abnormalities and determine when professional evaluation may be needed, thereby offering a practical option for individuals with limited access to in-person care.Description
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) March 6-9, 2026, Tucson, Arizona, U.S.A. Workshops, 2026, pp. 415-424
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