OMNI-Dent：迈向一个可及且可解释的AI框架，用于自动化牙科诊断
OMNI-Dent: Towards an Accessible and Explainable AI Framework for Automated Dental Diagnosis
- University of Minnesota（明尼苏达大学）
- Khon Kaen University（科恩卡恩大学）
- Minnesota State University（明尼苏达州立大学）
- The University of Western Australia（西澳大学）
- Kaohsiung Medical University（高雄医学院）
- Auckland University of Technology（奥克兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
OMNI-Dent通过结合临床推理原则和视觉语言模型，提供一个数据高效且可解释的牙科诊断框架，帮助用户识别异常并判断是否需要专业评估。
AI中文摘要：
准确的牙科诊断对于口腔健康至关重要，但许多人缺乏及时专业评估的渠道。现有的基于AI的方法主要将诊断视为视觉模式识别任务，并不反映牙科专业人员使用的结构化临床推理。这些方法还需要大量的专家标注数据，且往往难以在多样化的现实世界成像条件下泛化。为了解决这些限制，我们提出了OMNI-Dent，一个数据高效且可解释的诊断框架，该框架将临床推理原则融入基于视觉语言模型（VLM）的流程中。该框架基于多视角智能手机照片运行，嵌入来自牙科专家的诊断启发式方法，并引导通用的VLM进行牙级评估，而无需对VLM进行牙科特定的微调。通过利用VLM已有的视觉-语言能力，OMNI-Dent旨在支持在没有经过筛选的临床影像的情况下进行诊断评估。设计为一个早期阶段的辅助工具，OMNI-Dent帮助用户识别潜在的异常，并确定何时可能需要专业评估，为那些无法获得面对面护理的人提供了一种实用的选项。
英文摘要：
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.
