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

AI can incorporate clinical reasoning, not just pattern recognition: OMNI-Dent moves beyond treating dental diagnosis as a simple visual recognition task by embedding actual diagnostic heuristics from dental experts into the system, reflecting how professionals actually think through cases rather than just matching images to patterns.

Smartphone photos can enable accessible diagnosis: The framework works with multi-view smartphone photographs rather than requiring specialized dental imaging equipment, potentially making preliminary dental assessment available to people in underserved areas or with limited access to professional care.

Less expert data needed for development: By leveraging existing general-purpose AI models (Vision-Language Models) without requiring extensive dental-specific fine-tuning, OMNI-Dent reduces the burden of collecting large amounts of expert-annotated dental data, which is often a bottleneck in developing medical AI systems.

Explainability supports appropriate use as an assistive tool: The framework is designed to help users identify potential abnormalities and determine when professional evaluation is needed, rather than replacing dentists—this positioning as an early-stage assistive tool with explainable reasoning helps set realistic expectations about what the AI can and cannot do.

Real-world conditions matter for responsible deployment: The research specifically addresses the challenge that existing AI methods "struggle to generalize across diverse real-world imaging conditions," acknowledging that responsible AI for healthcare must work reliably outside controlled laboratory settings.