❯ DAMO Academy open-sources RADAR, reading abdominal CT for about 150 conditions, published in Science

open sourcedAlibaba’s DAMO Academy has open-sourced RADAR, a medical vision-language model that reads contrast-enhanced abdominal CT to identify nearly 150 conditions, including malignant tumors, according to the South China Morning Post. It covers 18 abdominal organs, the paper appeared in Science, and the code went up on GitHub on September 18, a day after publication.

training and resultsMost medical AI to date has been single-disease detectors; RADAR takes the generalist route. Per the research team’s disclosures, it was trained on over 400,000 contrast-enhanced abdominal CT exams and 15 million anatomy-aware image-text pairs, learning directly from clinical reports rather than manual annotation. Across nearly 40,000 real-world exams it averaged an AUC of 0.913 over 146 clinical findings, and in a head-to-head study it outperformed 23 of 26 expert radiologists.

the licenseThe “open source” needs unpacking: the GitHub code is Apache 2.0, but the weights on Hugging Face carry CC BY-NC-SA 4.0 — research permitted, attribution and share-alike required, commercial deployment excluded. Hospitals and research institutions can use it directly; vendors wanting to build it into a product cannot.

what shiftsWhat changes is the capacity constraint in frontline radiology. A senior radiologist reading one contrast-enhanced abdominal CT organ by organ takes a long time, while a generalist model compresses 146 findings into a single inference pass. What to watch is whether the weight license loosens to permit commercial use — if it does not, this capability stays in papers and hospital internal systems and never reaches commercial imaging equipment.

▪ SIGNALBeating 23 of 26 radiologists while barring commercial use — this release gave away the capability and kept the channel.