Evidence-Grounded Iterative Reasoning for the Diagnosis of Diffuse Cystic Lung Diseases

Researchers proposed DCLDs-EIR, an evidence-grounded iterative reasoning framework that couples diagnostic hypothesis refinement with multimodal evidence acquisition and verification to diagnose Diffuse Cystic Lung Diseases (DCLDs). The framework uses multiview renderings of predicted lung and cyst segmentations to characterize global cyst distributions, then guides axial CT inspection with competing hypotheses and retrieves disease-specific knowledge from a knowledge graph to support or refute each diagnosis. Multicenter evaluation showed DCLDs-EIR consistently improved diagnostic accuracy over baselines and produced traceable differential diagnoses linking hypotheses, patient observations, and disease-specific knowledge.

arXiv:2611.12981v1 Announce Type: new Abstract: Overlapping imaging manifestations complicate the differential diagnosis of Diffuse Cystic Lung Diseases DCLDs , while limited annotated data constrain supervised model development. Multimodal Large Language Models MLLMs can integrate imaging and clinical information, but their diagnostic reasoning remains limited by incomplete volumetric perception and insufficient grounding in disease-specific evidence. We propose DCLDs-EIR, an evidence-grounded iterative reasoning framework coupling diagnostic hypothesis refinement with multimodal evidence acquisition and verification. Multiview renderings of predicted lung and cyst segmentations characterize global cyst distributions and initialize the differential diagnosis. Competing hypotheses guide axial CT inspection to identify local findings that, together with clinical context, inform retrieval from a disease-specific knowledge graph. Accumulated patient observations and retrieved knowledge are jointly assessed for diagnostic support and refutation to revise the diagnostic state. Updated hypotheses and unresolved distinctions guide subsequent inspection, forming a feedback loop between evidence acquisition and hypothesis verification. Multicenter evaluation demonstrates that DCLDs-EIR consistently improves diagnostic accuracy over baselines and enables traceable differential diagnosis through explicit links between diagnostic hypotheses, patient observations, and disease-specific knowledge.