DelibRRG: Trustworthy Cross-Examination and Clinical-First Deliberation for Radiology Report Generation

Radiology report generation (RRG) aims to generate accurate and standardized diagnostic descriptions from medical images. Existing methods improve report quality through stronger representation learning and cross-modal interaction, but typically lack a stable clinical reference and an explicit mechanism for coordinating diagnostic and linguistic objectives. As a result, diagnostic roles may lose functional distinction during semantic interaction, while clinical fact preservation can be weakened by competing generation objectives. Inspired by information structure theory and multi-expert decision-making, we propose Trustworthy Cross-Examination and Clinical-First **Delib**eration for **R**adiology **R**eport **G**eneration (**DelibRRG**), which reformulates RRG as a clinical deliberation process organized by clinical reference establishment, heterogeneous role interaction, and asymmetric coordination. DelibRRG first employs dual-space structural calibration to construct a trustworthy clinical reference by calibrating candidate cross-modal correspondences through neighborhood consensus. It then constructs functionally decoupled clinical and semantic streams that exchange complementary information through controlled bidirectional cross-examination, followed by training-time clinical review of the updated diagnostic representation. Finally, clinical-first asymmetric bargaining assigns unequal bargaining power and a clinical disagreement point to bias joint optimization toward clinical fact preservation. Experiments on MIMIC-CXR and IU-Xray demonstrate strong report generation performance, while the clinical efficacy gains on MIMIC-CXR suggest improved clinical factual accuracy.