In this work, we use an Eye-Gaze dataset along with radiology reports to exhaustively study the impact of adding radiology reports and Eye-Gaze data to X-ray images on the performance and explainability of CNN-based classification models. Additionally, we introduce an explainability metric to quantitatively evaluate the alignment of model attention with radiologist-specified regions of interest (ROIs). Published in MICCAI Workshop Proceedings.