NVIDIA released NV-Reason-CT, an open research model that reads 3D CT scans and writes structured reports with step-by-step reasoning.

What NVIDIA released

NVIDIA described NV-Reason-CT on its technical blog on 23 Sep 2026. It is a vision language model that looks at a full 3D CT scan of the chest or abdomen. It writes a structured report, shows its reasoning step by step, and can answer follow-up questions about the same scan.

It builds on NV-Reason-CXR, an earlier NVIDIA reasoning model for chest X-rays. NIH radiologists reviewed the new model's reports and reasoning traces, the blog says.

How it works

The model pairs a Qwen3.5-4B language model with a 3D image encoder. Each scan is resized to a 192 x 192 x 192 grid at 2 mm spacing and cut into small cubes. That gives 13,824 image tokens, and all of them go to the language model with their 3D position, without merging.

Training had two stages. First came about 550,000 question-and-answer examples on chest and abdomen scans, drawn from CT-RATE, NIH CT datasets and CancerVerse, plus reasoning notes dictated by radiologists. Then reinforcement learning rewarded correct findings and diagnoses. Its list of findings covers 30 chest and 29 abdominal abnormalities.

How it scored

On the public CT-RATE test with 18 labels, NVIDIA reports a macro-F1 score of 0.614 and a macro-AUROC of 0.871. That puts it ahead of the other models in NVIDIA's table, such as VoxelFM (0.581 F1) and CT-CLIP (0.398 F1). These results come from one public dataset only.

Limits and what comes next

NVIDIA calls the model "not an autonomous diagnostic system or a cleared clinical product". It is meant as a base that researchers and developers train further for their own uses. The blog names firms such as Aidoc, Rad AI and Raidium as examples of companies that could adapt it.

The blog post does not state a licence. Anyone planning to use the model should check the terms on its Hugging Face and GitHub pages first.