Alibaba's Damo Academy open-sources Damo Radar, an AI that reads abdominal CT scans

Alibaba's research arm, Damo Academy, has open-sourced Damo Radar, a vision-language model that reads contrast-enhanced CT scans and flags 146 clinical findings across 18 abdominal organs, malignant tumours among them. Across nearly 40,000 real-world examinations it reached a mean area under the curve (AUC) of 0.913, where 1.0 is perfect. The research was published in the journal Science, and the code sits on GitHub under Apache 2.0. Alibaba owns the South China Morning Post, which reported the release; a link post on r/LocalLLaMA drew roughly 600 points and more than 40 comments.

The repository documents the scale behind it: 420,000 contrast-enhanced abdominal CT examinations and 15 million anatomy-aware image-text pairs, assembled from existing clinical reports rather than manual annotation. Download scripts fetch checkpoints from Hugging Face.

What the headline numbers do not say

AUC 0.913 is an average over 146 findings. One mean can hide findings where the model is far weaker, and it measures how well the model ranks cases, not a diagnosis any patient received. The reader study is aggregate in the same way: against 26 radiologists from multiple hospitals, the model's average accuracy exceeded 23 of them — an average-accuracy comparison, not a case-by-case win, and not a trial of clinical outcomes. The companion figures, 10% fewer missed diagnoses and reads more than 30% faster, are the study's own measurements, taken with the model acting as a second reader.

Open weights are not clearance

Nothing published with the release indicates FDA clearance or an equivalent approval in any jurisdiction, and "the world's first expert-level generalist medical imaging model" is a description the research team applies to its own work. The validated input is narrower than "medical imaging": contrast-enhanced abdominal CT, a protocol requiring an intravenous contrast agent, on a Chinese hospital population. Non-contrast scans, other contrast phases, other body regions and paediatric cases sit outside the tested scope. The licence is not unrestricted either: the code is Apache 2.0, but the weights ship under CC BY-NC-SA 4.0, which permits research use and blocks commercial deployment without a separate agreement.

What would settle it

Prospective trials that track model-assisted readings against patient outcomes, external validation across countries and patient populations, and an actual regulatory submission. Damo says the organ-level training method should extend to other imaging types, starting with chest CT and brain MRI; none of that has been validated yet. The April Coca model, said to be more sensitive than radiologists at spotting early colorectal cancer from CT, travelled the same route — a paper, a claim, and clinical proof still pending.