Vision Transformer-based classifier that detects abnormal left-ventricular ejection fraction (LVEF < 50%) directly from static, non-gated, non-contrast chest CT scans -- an imaging modality ordered for unrelated indications (lung cancer screening, pulmonary embolism, trauma) in over 80 million US exams a year -- as a form of opportunistic heart-failure screening. Fine-tunes the encoder of the CT-ViT (GenerateCT) framework, with separate spatial (axial-plane) and z-axis (slice-wise) self-attention blocks, on 3D CT volumes paired with echocardiogram-derived LVEF labels from 25,948 Columbia University studies; reaches an AUROC of 0.786 on a held-out test set and 0.762 on external validation at Weill Cornell Medicine, clearly outperforming demographic/diagnosis-code-only baselines (Random Forest, XGBoost, AUROC 0.54-0.61). On a radiologist-comparison subset, the model's weighted F1 (0.80-0.81) exceeded two board-certified thoracic radiologists (0.62-0.80) at a small fraction of the interpretation time. Grad-CAM saliency maps highlighted clinically sensible correlates of reduced LVEF (cardiomegaly, dilated superior vena cava, calcified ascending aorta, pacemaker hardware, pulmonary edema). No public code or model weights have been released.