Synthetic CT as a First Safeguard for Choosing Medical VLMs in Lung Cancer Screening
Many medical vision--language models (VLMs) are publicly available, but a site adopting one for a new imaging task rarely has the labeled real data needed to compare them. Because synthetic CT has ground truth by construction, a site could instead rank candidate models on synthetic scans, if the ranking carries over to real data. We study when it does for lung CT, using five public VLMs and three routine screening tasks, namely detecting a nodule, locating its lobe, and estimating its size, on 85,021 synthetic and 26,085 real slices from seven datasets. For nodule presence and size, the synthetic ranking matches the real one with a rank correlation of 0.98. For lobe, the ranking is only m...
| Authors | Fakrul Islam Tushar, Geoffrey D. Rubin |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 13 October 2026 |
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