RadFabric: Agentic AI System with Reasoning Capability for Radiology
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic
conditions, but current automated systems face limitations in pathology
coverage, diagnostic accuracy, and integration of visual and textual reasoning.
To address these gaps, we propose RadFabric, a multi agent, multimodal
reasoning framework that unifies visual and textual analysis for comprehensive
CXR interpretation. RadFabric is built on the Model Context Protocol (MCP),
enabling modularity, interoperability, and scalability for seamless integration
of new diagnostic agents. The system employs specialized CXR agents for
pathology detection, an Anatomical Interpretation Agent to map visual findings
to precise anatomical structures, and a Reasoning Agent powered by large
multimodal reasoning models to synthesize visual, anatomical, and clinical data
into transparent and evidence based diagnoses. RadFabric achieves significant
performance improvements, with near-perfect detection of challenging
pathologies like fractures (1.000 accuracy) and superior overall diagnostic
accuracy (0.799) compared to traditional systems (0.229 to 0.527). By
integrating cross modal feature alignment and preference-driven reasoning,
RadFabric advances AI-driven radiology toward transparent, anatomically
precise, and clinically actionable CXR analysis.
