OMNI-Dent: Towards an Accessible and Explainable AI Framework for Automated Dental Diagnosis

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.