Auditable CT Phenotyping Through Report-derived Radiological Observations

2026-08-26

AI Summary

研究使用基于报告的放射学观察构建的可审计CT表型（ACT）方法，解决了医学图像基础模型在预测临床表型时依赖疾病特异性发现还是捷径的问题。

Abstract

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 pulmonary angiography, both under zero-shot scoring (0.651 versus 0.572) and under linear probing (0.709 versus 0.662). Reading each probe exposes what accuracy conceals: only 97 observations occupy the 221 rank-1 positions, and one phrase describing aortic and coronary calcification ranks first for 20 phenotypes, including osteoporosis, urinary tract infection and major depressive disorder. Restricting the bank to clinician-specified evidence redirects those probes onto phenotype-related observations in 86 phenotypes at no accuracy cost (0.751 versus 0.741). Accurate CT-based EHR phenotyping can therefore rest on observations that are not valid evidence for the coded phenotype and that ACT can identify and intervene on.

Problem

• Computed Tomography
• Clinical Phenotypes
• Radiological Observations
• Auditable CT phenotyping
• Electronic Health Record

Innovation

• Auditable CT phenotyping (ACT)
• report-derived radiological observations
• zero-shot annotation
• linear probing
• clinician-specified evidence