DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning

2026-08-19

AI Summary

为解决多模态牙科诊断准确性问题，提出DentAgent框架，通过协调五个专业代理处理不同数据类型，并利用共享证据状态提高诊断的可追溯性和准确性。

Abstract

Oral diseases affect billions of people worldwide, underscoring a pressing need for accurate and reliable dental assessment that integrates heterogeneous evidence from domain knowledge, radiographs, intraoral photographs, and 3D dental data. Most existing dental AI systems remain modality- or task-specific. Although recent vision-language models support flexible dental question answering, directly generated response leaves evidence implicit and untraceable. To address these limitations, we introduce DentAgent, an evidence-centric multi-agent framework, in which the Orchestrator coordinate five specialized agents spanning various modalities. Each specialist utilizes domain tools to convert observations into structured evidence records. The Evidence Blackboard manages these records as a shared evidence state, tracking coverage, gaps, and conflicts before response generation. This standardized evidence representation integrates isolated dental capabilities into a unified agentic workflow. Across four benchmarks, DentAgent demonstrates leading performance, even surpassing the senior specialists by 17.3 percentage points on multi-label diagnosis, which supports its value for broadly applicable and traceable multimodal dental reasoning, and highlights its potential as a technical foundation for population oral health assessment and management.

Problem

• Multimodal Dental Reasoning
• Evidence-Centric
• Multi-Agent Coordination

Innovation

• evidence-centric
• multi-agent coordination
• multimodal dental reasoning
• shared evidence state