  The automatic clinical caption generation problem is referred to as proposed
model combining the analysis of frontal chest X-Ray scans with structured
patient information from the radiology records. We combine two language models,
the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive
radiology records. The proposed combination of these models generates a textual
summary with the essential information about pathologies found, their location,
and the 2D heatmaps localizing each pathology on the original X-Ray scans. The
proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and
the general-purpose MS-COCO. The results measured with the natural language
assessment metrics prove their efficient applicability to the chest X-Ray image
captioning.
