  The capabilities of AI for biomedicine span a wide spectrum, from the atomic
level, where it solves partial differential equations for quantum systems, to
the molecular level, predicting chemical or protein structures, and further
extending to societal predictions like infectious disease outbreaks. Recent
advancements in large language models, exemplified by models like ChatGPT, have
showcased significant prowess in natural language tasks, such as translating
languages, constructing chatbots, and answering questions. When we consider
biomedical data, we observe a resemblance to natural language in terms of
sequences: biomedical literature and health records presented as text,
biological sequences or sequencing data arranged in sequences, or sensor data
like brain signals as time series. The question arises: Can we harness the
potential of recent large language models to drive biomedical knowledge
discoveries? In this survey, we will explore the application of large language
models to three crucial categories of biomedical data: 1) textual data, 2)
biological sequences, and 3) brain signals. Furthermore, we will delve into
large language model challenges in biomedical research, including ensuring
trustworthiness, achieving personalization, and adapting to multi-modal data
representation
