  Pre-trained language models (PLMs) have been the de facto paradigm for most
natural language processing (NLP) tasks. This also benefits biomedical domain:
researchers from informatics, medicine, and computer science (CS) communities
propose various PLMs trained on biomedical datasets, e.g., biomedical text,
electronic health records, protein, and DNA sequences for various biomedical
tasks. However, the cross-discipline characteristics of biomedical PLMs hinder
their spreading among communities; some existing works are isolated from each
other without comprehensive comparison and discussions. It expects a survey
that not only systematically reviews recent advances of biomedical PLMs and
their applications but also standardizes terminology and benchmarks. In this
paper, we summarize the recent progress of pre-trained language models in the
biomedical domain and their applications in biomedical downstream tasks.
Particularly, we discuss the motivations and propose a taxonomy of existing
biomedical PLMs. Their applications in biomedical downstream tasks are
exhaustively discussed. At last, we illustrate various limitations and future
trends, which we hope can provide inspiration for the future research of the
research community.
