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Anyone know how many parameters?
The model weights are only ~5GB, so this is small.
Approx ~131 million total. Made up of estimated:
102M - modified BERT-base-Chinese text encoder
26M - 3D U-Net-style vision/anatomy encoder
2.8M - projection layers, anatomy-specific projections, query tokens and attention layer
Written to run on something like an H100 though- as the CT Scan data is quite large.
ScholarlyArticle: "Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial" (2026) Nature Medicine https://www.nature.com/articles/s41591-026-04589-y
Source: https://github.com/alibaba-damo-academy/damo-radar
Model: https://huggingface.co/radar-generalist
From https://news.ycombinator.com/item?id=44693991 :
“A Novel Exopolysaccharide, Highly Prevalent in Marine Spongiibacter, Triggers Pyroptosis to Exhibit Potent Anticancer Effects” (2025) DOI: 10.1096/fj.202500412R https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.2025004...
"A Gemma model helped discover a new potential cancer therapy pathway" https://news.ycombinator.com/item?id=45604231 :
"Scientists are discovering a powerful new way to prevent cancer" https://news.ycombinator.com/item?id=45474404
Notes re: Kidneys not Livers: https://news.ycombinator.com/item?id=47460486 ; gh/topic/healthcare-ai
[flagged]
you hit the nail right on the head!
I'm wonder when there will be model for predict future. Could be named "Crystal bAill"
Isn't detecting cancer predicting a possible future? Hopefully one you can change.
The attempt is already there: "predicting the future" is called "intelligence".
It is 2026. How are we still publishing articles on medical diagnostics data science and using area under the ROC curve as the primary metric of success. ROC-AUC of 0.9 under severe class imbalance (almost always the case in diagnostics) could still mean something like 4/5 predicted diagnoses are wrong (false positives). Precision-Recall curve + mAP or GTFO.
Science article in question: https://www.science.org/doi/abs/10.1126/science.aec6129
Also, the most interesting result here is that the CNN-based feature encoder significantly outperformed a vision transformer encoder backbone…
Waste avoidance.
Bullsh*t is more than sufficient to convince an AI-gulled target audience.
Teaching students how to interpret evidence must be way undervalued still. I went to one of the top CS schools 20 years ago and you could get a degree without even taking a single probability or stats class of any kind.
I don't agree with the flak Chinese labs get. If it's really that easy to distill and compete with frontier models, why aren't other countries anywhere near this AI race?
Because distillation is a friendly term for industrial espionage, and most other countries are not willing to become international pariahs in the eyes of the west.
meanwhile in the real world https://www.science.org/content/article/china-tops-world-art...
both things can be true. China's gearing up, but they also try to make progress by aggressively distilling Anthropic and OpenAI models, and this is currently where most of their progress comes from.