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

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.