  Current approaches to explaining the decisions of deep learning systems for
medical tasks have focused on visualising the elements that have contributed to
each decision. We argue that such approaches are not enough to "open the black
box" of medical decision making systems because they are missing a key
component that has been used as a standard communication tool between doctors
for centuries: language. We propose a model-agnostic interpretability method
that involves training a simple recurrent neural network model to produce
descriptive sentences to clarify the decision of deep learning classifiers.
  We test our method on the task of detecting hip fractures from frontal pelvic
x-rays. This process requires minimal additional labelling despite producing
text containing elements that the original deep learning classification model
was not specifically trained to detect.
  The experimental results show that: 1) the sentences produced by our method
consistently contain the desired information, 2) the generated sentences are
preferred by doctors compared to current tools that create saliency maps, and
3) the combination of visualisations and generated text is better than either
alone.
