  Image-to-text radiology report generation aims to automatically produce
radiology reports that describe the findings in medical images. Most existing
methods focus solely on the image data, disregarding the other patient
information accessible to radiologists. In this paper, we present a novel
multi-modal deep neural network framework for generating chest X-rays reports
by integrating structured patient data, such as vital signs and symptoms,
alongside unstructured clinical notes.We introduce a conditioned
cross-multi-head attention module to fuse these heterogeneous data modalities,
bridging the semantic gap between visual and textual data. Experiments
demonstrate substantial improvements from using additional modalities compared
to relying on images alone. Notably, our model achieves the highest reported
performance on the ROUGE-L metric compared to relevant state-of-the-art models
in the literature. Furthermore, we employed both human evaluation and clinical
semantic similarity measurement alongside word-overlap metrics to improve the
depth of quantitative analysis. A human evaluation, conducted by a
board-certified radiologist, confirms the model's accuracy in identifying
high-level findings, however, it also highlights that more improvement is
needed to capture nuanced details and clinical context.
