This study addresses two key challenges in structuring radiology reports: the lack of a practical structuring schema and datasets to evaluate model generalizability. To address these challenges, we propose a “Finding-Centric Structuring,” which organizes reports around individual findings, facilitating secondary use. We also construct JRadFCS, a large-scale dataset with annotated named entities (NEs) and relations, comprising 8,428 Japanese Computed Tomography (CT) reports from seven facilities, providing a comprehensive resource for evaluating model generalizability. Our experiments reveal performance gaps when applying models trained on single-facility reports to those from other facilities. We further analyze factors contributing to these gaps and demonstrate that augmenting the training set based on these performance-correlated factors can efficiently enhance model generalizability.

Radiology report generation systems have the potential to reduce the workload of radiologists by automatically describing the findings in medical images.To broaden the application of the report generation system, the system should generate reports that are not only factually accurate but also chronologically consistent, describing images that are presented in time order, that is, the correct order.We employ a planning-based radiology report generation system that generates the overall structure of reports as “plans’” prior to generating reports that are accurate and consistent in order.Additionally, we propose a novel reinforcement learning and inference method, Coordinated Planning (CoPlan), that includes a content planner and a text generator to train and infer in a coordinated manner to alleviate the cascading of errors that are often inherent in planning-based models.We conducted experiments with single-phase diagnostic reports in which the factual accuracy is critical and multi-phase diagnostic reports in which the description order is critical.Our proposed CoPlan improves the content order score by 5.1 pt in time series critical scenarios and the clinical factual accuracy F-score by 9.1 pt in time series irrelevant scenarios, compared those of the baseline models without CoPlan.