  Radiologists are tasked with interpreting a large number of images in a daily
base, with the responsibility of generating corresponding reports. This
demanding workload elevates the risk of human error, potentially leading to
treatment delays, increased healthcare costs, revenue loss, and operational
inefficiencies. To address these challenges, we initiate a series of work on
grounded Automatic Report Generation (AutoRG), starting from the brain MRI
interpretation system, which supports the delineation of brain structures, the
localization of anomalies, and the generation of well-organized findings. We
make contributions from the following aspects, first, on dataset construction,
we release a comprehensive dataset encompassing segmentation masks of anomaly
regions and manually authored reports, termed as RadGenome-Brain MRI. This data
resource is intended to catalyze ongoing research and development in the field
of AI-assisted report generation systems. Second, on system design, we propose
AutoRG-Brain, the first brain MRI report generation system with pixel-level
grounded visual clues. Third, for evaluation, we conduct quantitative
assessments and human evaluations of brain structure segmentation, anomaly
localization, and report generation tasks to provide evidence of its
reliability and accuracy. This system has been integrated into real clinical
scenarios, where radiologists were instructed to write reports based on our
generated findings and anomaly segmentation masks. The results demonstrate that
our system enhances the report-writing skills of junior doctors, aligning their
performance more closely with senior doctors, thereby boosting overall
productivity.
