FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis

2026-08-22

AI Summary

本文提出FRAC-MAS系统，通过结合视觉模型和多代理工作流解决骨骨折诊断的可解释性和安全性问题，提高了诊断的准确性与临床适用性。

Abstract

Fracture detection and its clinical interpretability see notable improvements when deep vision models are integrated with agentic AI architectures. While deep learning models achieve high diagnostic performance, their black-box nature limits clinical adoption. We propose FRAC-MAS, an agentic AI system for automated, explainable, and safe bone fracture detection. The framework combines a stacked ensemble of four vision models with conformal prediction to produce statistically grounded differential diagnoses, while a multi-agent workflow performs independent verification, retrieves clinical guidelines, and generates patient-friendly reports. A pipeline-depth ablation study confirms that our multi-agent critic triages 86.6% of cases into a high-confidence auto-confirmed cohort while escalating uncertain cases, outperforming a single-agent baseline. Patient preference studies against Llama, MedGemma, and Gemini further demonstrate significantly more comprehensible clinical reports. These results suggest that integrating multi-agent critics with conformal guarantees enables safer radiology triage while preserving clinician oversight. More broadly, FRAC-MAS demonstrates how cooperative agentic architectures can serve as auditable, human-in-the-loop decision support systems for safety-critical healthcare. Our code is available at https://github.com/hardik1712/FRAC-MAS, and the website is available at https://frac-mas.vercel.app.

Problem

• Fracture Detection
• Clinical Interpretability
• Explainable AI
• Safety
• Multi-Agent Systems

Innovation

• multi-agent system
• explainable AI
• conformal prediction
• bone fracture detection
• clinical interpretability