ChatGPT has drawn considerable attention from both the general public and
domain experts with its remarkable text generation capabilities. This has
subsequently led to the emergence of diverse applications in the field of
biomedicine and health. In this work, we examine the diverse applications of
large language models (LLMs), such as ChatGPT, in biomedicine and health.
Specifically we explore the areas of biomedical information retrieval, question
answering, medical text summarization, information extraction, and medical
education, and investigate whether LLMs possess the transformative power to
revolutionize these tasks or whether the distinct complexities of biomedical
domain presents unique challenges. Following an extensive literature survey, we
find that significant advances have been made in the field of text generation
tasks, surpassing the previous state-of-the-art methods. For other
applications, the advances have been modest. Overall, LLMs have not yet
revolutionized the biomedicine, but recent rapid progress indicates that such
methods hold great potential to provide valuable means for accelerating
discovery and improving health. We also find that the use of LLMs, like
ChatGPT, in the fields of biomedicine and health entails various risks and
challenges, including fabricated information in its generated responses, as
well as legal and privacy concerns associated with sensitive patient data. We
believe this first-of-its-kind survey can provide a comprehensive overview to
biomedical researchers and healthcare practitioners on the opportunities and
challenges associated with using ChatGPT and other LLMs for transforming
biomedicine and health.