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

TL;DR

OMNI-Dent is an explainable, data-efficient AI framework that uses clinical reasoning principles and vision-language models to improve dental diagnosis from smartphone images, especially in resource-limited settings.

Contribution

It introduces a novel, explainable diagnostic pipeline that embeds expert heuristics into a vision-language model without requiring dental-specific fine-tuning.

Findings

Supports tooth-level evaluation from smartphone images

Operates without extensive annotated dental datasets

Provides explainable diagnostic suggestions

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. To address these limitations, 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 utilizing the VLM's existing visual-linguistic capabilities, OMNI-Dent aims to support diagnostic assessment in settings where curated clinical imaging is unavailable. Designed as an early-stage assistive tool, OMNI-Dent helps users identify potential abnormalities and determine when professional evaluation may be needed, offering a practical option for individuals with limited access to in-person care.