SOURCE-LINKED INTELLIGENCE

Auditable CT Phenotyping Through Report-derived Radiological Observations

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or shortcuts that correlate with the diagnosis. We tested this in 221 electronic-health-record (EHR) phenotypes using Auditable CT phenotyping (ACT), built on report-derived radiological observations. We trained ACT on 38,317 patients, mined 376,194 observations and evaluated it in 25,183 held-out patients. ACT exceeded five vision-language baselines on zero-shot annotation, and CT-CLIP across 221 phenotypes from unseen CT

Read original source ↗ Open in workspace

recordType

paper

region

Global