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Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically faithful generation remains challenging because current supervision is still imposed mostly at the report level. This creates a granularity mismatch: radiology reports are composed of disease-grounded findings, while existing methods are trained mainly with whole-report objectives. To address this problem, we propose Graph-Supervised Hierarchical Clinical Alignment, which reformulates image-report supervision as a hierarchical clinical alignment p

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