RADAR brings generalist abdominal-CT diagnosis to a published study and released research model
A Science study presents RADAR, a vision-language model trained on more than 400,000 contrast-enhanced abdominal CT examinations and 15 million anatomy-specific image–text pairs drawn from clinical reports. The authors evaluated 146 imaging findings across 18 anatomical structures in internal and external multicenter tests, and report that RADAR assistance increased diagnostic sensitivity by approximately 10% in a study of 26 radiologists.
Why it made the cut: Peer-reviewed multicenter evidence, a human-reader study, and released checkpoints and training/inference code distinguish this from a narrow diagnostic benchmark claim. It offers researchers a generalist starting point across many abdominal findings, but the repository restricts use to research, uses a noncommercial license, and explicitly calls for prospective clinical studies before deployment; the reported reader-study gain is not evidence of improved patient outcomes.
Paper (Science) · Author abstract (PubMed) · Official project announcement and code · Model checkpoints and supporting data