The adaptation of Large Language Models (LLMs) to specialized domains such as biomedicine holds significant promise for advancing Natural Language Processing (NLP) applications in healthcare and medical research. In this survey, we investigate various techniques and approaches for adapting LLMs to better understand and process biomedical text. We explore the challenges faced by LLMs in biomedical tasks, including the presence of specialized terminology and the scarcity of annotated datasets. The survey discusses adaptation techniques such as training LLMs from scratch, fine-tuning on biomedical data, and injecting domain-specific knowledge. Furthermore, we compare the efficacy of these techniques and highlight their advantages and challenges. Our examination reveals that while each approach offers distinct benefits, the choice depends on factors such as available resources, task requirements, and desired performance outcomes. Finally, we outline future research directions focusing on knowledge integration in the biomedical domain to enhance the capabilities of LLMs and their potential impact on healthcare and medical research.