  The COVID-19 has brought about a significant challenge to the whole of
humanity, but with a special burden upon the medical community. Clinicians must
keep updated continuously about symptoms, diagnoses, and effectiveness of
emergent treatments under a never-ending flood of scientific literature. In
this context, the role of evidence-based medicine (EBM) for curating the most
substantial evidence to support public health and clinical practice turns
essential but is being challenged as never before due to the high volume of
research articles published and pre-prints posted daily. Artificial
Intelligence can have a crucial role in this situation. In this article, we
report the results of an applied research project to classify scientific
articles to support Epistemonikos, one of the most active foundations worldwide
conducting EBM. We test several methods, and the best one, based on the XLNet
neural language model, improves the current approach by 93\% on average
F1-score, saving valuable time from physicians who volunteer to curate COVID-19
research articles manually.
