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

• URL: http://arxiv.org/abs/2608.28662v1
• Date: Sat, 22 Aug 2026 10:56:00 GMT
• Title: FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis
• Abstract summary: FRAC-MAS is an agentic AI system for automated, explainable, and safe bone fracture detection.<n>A pipeline-depth ablation study confirms that our multi-agent critic triages 86.6% of cases into a high-confidence auto-confirmed cohort.<n>Patient preference studies against Llama, MedGemma, and Gemini demonstrate significantly more comprehensible clinical reports.
• Score: 0.6351973754158782
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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