COVID-19 has brought about a significant challenge to the whole of humanity, but mainly to 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 especially essential but is being challenged as never before. 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 essential foundations worldwide conducting EBM. We test several methods, and the best one, based on XLNet, improves the current approach by 93% on average F1-score, saving valuable time from physicians who volunteer to curate COVID-19 research articles manually.