RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
This work addresses the need for clinically interpretable and consistent AI systems in radiology, though it appears incremental by building on existing agentic and multimodal methods.
The paper tackled the problem of chest X-ray interpretation by addressing limitations in clinical interpretability, multimodal fusion, and inconsistency resolution, resulting in a multi-agent framework that produces more reliable and transparent outputs aligned with clinical guidelines.
Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use and external knowledge bases. Nevertheless, for chest X-ray (CXR) interpretation, prevailing methods remain limited: (i) reasoning is frequently neither clinically interpretable nor aligned with guidelines, reflecting mere aggregation of tool outputs; (ii) multimodal evidence is insufficiently fused, yielding text-only rationales that are not visually grounded; and (iii) systems rarely detect or resolve cross-tool inconsistencies and provide no principled verification mechanisms. To bridge the above gaps, we present RadAgents, a multi-agent framework for CXR interpretation that couples clinical priors with task-aware multimodal reasoning. In addition, we integrate grounding and multimodal retrieval-augmentation to verify and resolve context conflicts, resulting in outputs that are more reliable, transparent, and consistent with clinical practice.