构建多智能体系统，让口腔诊疗推理更可追溯、更准确。
DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
- 用五个专精智能体协同处理影像、照片等多模态数据
- 在多标签诊断任务上超越专家模型17.3个百分点
- 适合需要可解释性医疗辅助的临床与研究场景
全球数十亿人受口腔疾病影响，亟需整合领域知识、牙片、口内照片和3D牙科数据的精准评估。现有AI系统多局限于单一模态或任务，虽有视觉-语言模型支持问答，但生成结果缺乏显式证据且不可追溯。为此，我们提出DentAgent——一个以证据为中心的多智能体框架，由协调器调度五个跨模态专精智能体。每个智能体使用领域工具将观测转化为结构化证据记录。证据黑板作为共享状态，追踪证据覆盖、缺失与冲突，再生成最终响应。该标准化表示将孤立的牙科能力整合为统一代理流程。在四个基准测试中，DentAgent表现领先，尤其在多标签诊断任务上优于资深专家模型17.3个百分点，验证其在可追溯、通用性强的多模态牙科推理中的价值，具备支撑人群口腔健康评估与管理的技术潜力。
原文摘要 · Abstract (English)
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.
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