During the COVID-19 pandemic, a huge amount of literature was produced covering different aspects of infection. The use of artificial intelligence (AI) in medical imaging has been shown to improve screening, diagnosis, treatment, and medication for the COVID-19 virus. Applying natural language processing (NLP) solutions to COVID-19 literature has contributed to infer significant COVID-19-related topics and correlated diseases. In this paper, we aim at evaluating biomedical transformer-based NLP techniques in COVID-19 research to understand if they are able to classify problems related to COVID-19. Particularly, once collected COVID-19 publications encompassing the terms AI and medical imaging, fifteen BERT-based models have been compared with respect to modality prediction and task prediction.
