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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Zijie Meng, Xiwei Dai, Yixuan Tang, Jin Hao, Yang Feng, Fudong Zhu, Xiaoqiang Liu, Shaosheng Cao, Zuozhu Liu
• Venue: arXiv: cs.AI
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18878v1
• PDF: https://arxiv.org/pdf/2608.18878v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

Researchers have developed DentAgent, an evidence-centric multi-agent framework to address the limitations of existing dental AI systems. This system integrates various modalities like domain knowledge, radiographs, intraoral photographs, and 3D dental data for accurate and reliable dental assessment. It outperforms senior specialists across four benchmarks, making it a promising foundation for population oral health assessment and management.

What the Document Says

• The study presents DentAgent, an evidence-centric multi-agent framework.
• DentAgent coordinates five specialized agents spanning various modalities.
• Each specialist uses domain tools to convert observations into structured evidence records.
• The Evidence Blackboard manages these records as a shared evidence state.
• Across four benchmarks, DentAgent demonstrates leading performance.
• DentAgent outperforms senior specialists by 17.3 percentage points on multi-label diagnosis.

Key Concepts Mentioned

• Oral diseases
• Dental assessment
• Multi-agent framework
• Evidence-centric
• Benchmarks
• Performance

Claims Made by the Document

• DentAgent addresses limitations in existing dental AI systems.
• DentAgent demonstrates leading performance across four benchmarks.
• DentAgent outperforms senior specialists by 17.3 percentage points on multi-label diagnosis.

Evidence Used

• The text states that DentAgent was developed to address the limitations in existing dental AI systems.
• The text provides results from four benchmarks showing DentAgent's leading performance.
• The text mentions a comparison between DentAgent and senior specialists, with a difference of 17.3 percentage points on multi-label diagnosis.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.