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

2026-02-03

AI Summary

Current AI-based dental diagnostic approaches rely heavily on large annotated datasets, lack structured clinical reasoning, and exhibit limited generalization under diverse imaging conditions. This work proposes an interpretable and data-efficient diagnostic framework that, for the first time, integrates expert-derived heuristic rules into a general-purpose vision-language model (VLM) to enable tooth-level assessment using multi-view smartphone images—without requiring domain-specific fine-tuning. By leveraging zero-shot and few-shot learning paradigms, the method achieves high usability and fairness even under non-standard imaging conditions, thereby facilitating early detection of oral abnormalities and supporting clinical decision-making in resource-constrained settings.

Technology Category

• Computer Vision: Large Vision Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• dental diagnosis
• clinical reasoning
• data efficiency
• explainable AI
• accessibility

Innovation

• Vision-Language Model
• Clinical Reasoning
• Explainable AI
• Data-Efficient Diagnosis
• Smartphone-Based Dental Imaging