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Science (New York, N.Y.)

An expert-level generalist AI for abdominal CT diagnosis: RADAR

This work developed RADAR, a generalist vision-language model trained on more than 400,000 contrast-enhanced abdominal CT examinations and 15 million anatomy-wise image-text pairs, learning directly from clinical reports without manual annotation, achieving high diagnostic performance and robust generalization across internal and external evaluations for 18 anatomical structures and 146 imaging findings, and increasing the diagnostic sensitivity of 26 radiologists by ~10% in a reader study.