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Reliable automated Chest X-ray (CXR) analysis is critical for clinical decision support, yet deep learning models often suffer from overconfidence, limited interpretability, and poor out-of-distribution robustness. We propose a framework towards trustworthy AI for multi-class CXR classification integrating a hybrid CNN–Transformer with bi-cross attention, uncertainty quantification, and neuro-symbolic reasoning. The architecture fuses EfficientNet-B3 and ViT-B/16 features via bi-cross attention, aligning local and global representations. Predictive trust is assessed using Expected Calibration Error (ECE), while class-conditional Mahalanobis distance enables out-of-distribution (OOD) detection. Explainability is enhanced by constraining Grad-CAM++ heatmaps within lung masks. A neuro-symbolic reasoning (NSR) module translates neural activations into human-readable diagnostic statements grounded in radiological priors. Experiments on a four-class CXR dataset achieve 96% accuracy, macro F1-score of 0.97, and strong calibration (ECE = 0.0328). For OOD detection, the method achieves AUROC = 0.97 on out-of-domain images. These results demonstrate that combining bi-cross attention, anatomy-aware explainability, and neuro-symbolic logic yields a transparent and robust framework – a step towards clinically trustworthy AI for medical imaging.
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