  The deployment of large language models (LLMs) within the healthcare sector
has sparked both enthusiasm and apprehension. These models exhibit the
remarkable capability to provide proficient responses to free-text queries,
demonstrating a nuanced understanding of professional medical knowledge. This
comprehensive survey delves into the functionalities of existing LLMs designed
for healthcare applications, elucidating the trajectory of their development,
starting from traditional Pretrained Language Models (PLMs) to the present
state of LLMs in healthcare sector. First, we explore the potential of LLMs to
amplify the efficiency and effectiveness of diverse healthcare applications,
particularly focusing on clinical language understanding tasks. These tasks
encompass a wide spectrum, ranging from named entity recognition and relation
extraction to natural language inference, multi-modal medical applications,
document classification, and question-answering. Additionally, we conduct an
extensive comparison of the most recent state-of-the-art LLMs in the healthcare
domain, while also assessing the utilization of various open-source LLMs and
highlighting their significance in healthcare applications. Furthermore, we
present the essential performance metrics employed to evaluate LLMs in the
biomedical domain, shedding light on their effectiveness and limitations.
Finally, we summarize the prominent challenges and constraints faced by large
language models in the healthcare sector, offering a holistic perspective on
their potential benefits and shortcomings. This review provides a comprehensive
exploration of the current landscape of LLMs in healthcare, addressing their
role in transforming medical applications and the areas that warrant further
research and development.
