Diagnostic Performance of Different AI Tools for Chest Radiography: A CT-Anchored Per-Abnormality Analysis in a Tertiary Referral Center

Background/Objectives: Artificial intelligence (AI) increasingly supports chest radiograph interpretation, but per-abnormality comparisons of commercial tools using CT-anchored reference standards remain limited. We compared two commercial AI tools using a CT-anchored, CXR-targeted reference framework.

Methods

We retrospectively reviewed chest radiographs obtained between 30 October 2023 and 10 October 2024. Each radiograph was paired with CT performed within 14 days or within 2 days for rapidly progressive findings. Seven abnormalities across five analysis categories were evaluated: fracture, nodule, pleural effusion, pneumothorax, and airspace disease (edema, atelectasis, opacity/consolidation). Radiologists first confirmed abnormalities on CT then assessed their visibility on the paired radiograph. Outputs from vendors A and B were recorded. Sensitivity was calculated for CT-confirmed, radiographically visible abnormalities; specificity, precision, and accuracy were calculated within the same detectability framework. Vendors were compared using McNemar’s test.