Accurate and trustworthy diagnosis from chest radiographs remains a critical challenge in medical imaging. In this work, we propose a robust deep learning framework based on the Conformer architecture to address multi-label classification of thoracic diseases using the CheXpert dataset. The Conformer, which integrates convolutional and self-attention mechanisms, is particularly well-suited for modeling both local and global dependencies in high-resolution medical images. Our approach incorporates three key pillars for clinical readiness: (i) robustness, evaluated through adversarial perturbations and sensitivity analysis to ensure resilience to input noise; (ii) uncertainty quantification, enabling calibrated confidence estimates essential for high-stakes decision-making; and (iii) explainability, achieved through feature attribution techniques. Extensive experiments demonstrate that our model not only achieves competitive performance across multiple pathologies but also maintains stable predictions under distributional shifts and perturbations. These results highlight the potential of hybrid transformer-based models as reliable tools for real-world medical imaging applications, bridging the gap between performance and deployability.