Explicit chain of diagnosis for CMR semantic interpretation via large reasoning model

Cardiac magnetic resonance (CMR) is the clinical gold standard for assessing cardiovascular conditions, yet translating image semantic representations into specific disease diagnoses remains challenging. Current large language models (LLMs) often lack the interpretability required for clinical trust. Here, we present CMR-R, a large reasoning model that utilizes CMR quantitative parameters and semantic descriptions from CMR reports as inputs to provide explicit, interpretable diagnostic chains for CMR semantic diagnosis. We curated a multi-center dataset of 16,104 cases and developed a multi-stage adaptive-learning training framework for enhancing reasoning ability, mitigating long-tail distribution bias, and enabling prospective continuous learning with unlabeled data. CMR-R achieved diagnostic accuracy (ACC: 0.858, AUC: 0.944) across eight categories, surpassing that of radiologists (with >10 years of experience) and other LLMs, including in rare diseases such as left ventricular non-compaction (LVNC) and cardiac amyloidosis. Furthermore, CMR-R reveals underlying associations between CMR imaging features and cardiac diseases. This work has the potential to enhance CMR diagnostic accuracy and improve clinical efficiency.