  Pre-trained language models such as ClinicalBERT have achieved impressive
results on tasks such as medical Natural Language Inference. At first glance,
this may suggest that these models are able to perform medical reasoning tasks,
such as mapping symptoms to diseases. However, we find that standard benchmarks
such as MedNLI contain relatively few examples that require such forms of
reasoning. To better understand the medical reasoning capabilities of existing
language models, in this paper we introduce DisKnE, a new benchmark for Disease
Knowledge Evaluation. To construct this benchmark, we annotated each positive
MedNLI example with the types of medical reasoning that are needed. We then
created negative examples by corrupting these positive examples in an
adversarial way. Furthermore, we define training-test splits per disease,
ensuring that no knowledge about test diseases can be learned from the training
data, and we canonicalize the formulation of the hypotheses to avoid the
presence of artefacts. This leads to a number of binary classification
problems, one for each type of reasoning and each disease. When analysing
pre-trained models for the clinical/biomedical domain on the proposed
benchmark, we find that their performance drops considerably.
