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 :

EPS3.9 also had significant anti-tumor effects in the mice with liver cancer and activated anti-tumor immune responses

“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 :

eCPMV VNPs + EPS3.9 + [...]

"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

I much prefer load-bearing discussions myself.

Isn't detecting cancer predicting a possible future? Hopefully one you can change.

model [to] predict future

The attempt is already there: "predicting the future" is called "intelligence".