FRAC-MAS：一种用于骨折诊断的安全可解释多智能体系统
FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis
浏览论文内容
中文总结 AI 辅助
FRAC-MAS是一种结合堆叠集成视觉模型、共形预测与多智能体评审员的骨折诊断系统，可实现安全可解释的放射学分诊，性能优于单智能体基线，生成更易理解的临床报告。
中文摘要 AI 辅助
将深度视觉模型与智能体AI架构相结合，骨折检测及其临床可解释性取得了显著进步。尽管深度学习模型实现了较高的诊断性能，但其黑箱特性限制了临床应用。我们提出FRAC-MAS，这是一种用于自动、可解释且安全的骨折检测的智能体AI系统。该框架将四个视觉模型的堆叠集成与共形预测相结合，以生成具有统计依据的鉴别诊断，同时多智能体工作流执行独立验证、检索临床指南并生成患者友好型报告。一项管道深度消融研究证实，我们的多智能体评审员将86.6%的病例分类为高置信度自动确认队列，同时对不确定病例进行升级处理，其性能优于单智能体基线。针对Llama、MedGemma和Gemini的患者偏好研究进一步表明，FRAC-MAS生成的临床报告更易理解。这些结果表明，将多智能体评审员与共形保障相结合，可在保留临床医生监督的同时实现更安全的放射学分诊。更广泛而言，FRAC-MAS证明了协作式智能体架构如何作为可审计、人在回路的决策支持系统，服务于安全关键型医疗领域。我们的代码可在该URL获取，网站可在该URL获取。
英文摘要
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.
发表机构
- Dwarkadas J Sanghvi College of Engineering（德瓦卡达斯J.桑吉维工程学院）
- University of Amsterdam（阿姆斯特丹大学）
- National University of Singapore（新加坡国立大学）
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
