  This paper introduces RTEx, a novel methodology for a) ranking radiography
exams based on their probability to contain an abnormality, b) generating
abnormality tags for abnormal exams, and c) providing a diagnostic explanation
in natural language for each abnormal exam. The task of ranking radiography
exams is an important first step for practitioners who want to identify and
prioritize those radiography exams that are more likely to contain
abnormalities, for example, to avoid mistakes due to tiredness or to manage
heavy workload (e.g., during a pandemic). We used two publicly available
datasets to assess our methodology and demonstrate that for the task of ranking
it outperforms its competitors in terms of NDCG@k. For each abnormal
radiography exam RTEx generates a set of abnormality tags alongside an
explanatory diagnostic text to explain the tags and guide the medical expert.
Our tagging component outperforms two strong competitor methods in terms of F1.
Moreover, the diagnostic captioning component of RTEx, which exploits the
already extracted tags to constrain the captioning process, outperforms all
competitors with respect to clinical precision and recall.
