Large Language Models (LLMs) are transforming healthcare by leveraging their ability to process vast amounts of medical text and data. They are being used to enhance efficiency, improve clinical decision-making, and streamline administrative tasks. This chapter explores the foundation and transformative role of LLMs in the healthcare sector, presenting their evolution, diverse applications, and critical implementation challenges. LLMs as advanced AI models adept at predicting and assembling word sequences to generate coherent text, answer complex inquiries, and execute intricate instructions. While LLMs possess broad general capabilities, their application to the highly specialized and critical domain of healthcare necessitates a tailored approach, demanding precision and domain-specific understanding. This adaptation includes the development of specialized domain-specific LLMs, which are fine-tuned on medical texts and Electronic Health Records (EHRs) to enhance natural language processing, improve research accessibility, provide targeted question answering, and support computer-aided diagnosis. Research demonstrates that the accuracy of LLMs in certain areas can match or even surpass the quality produced by human experts. LLMs can help reducing documentation burdens and aid efficient information retrieval from medical records. Furthermore, LLMs are poised to revolutionize medical education and research, streamlining the entire research process from initial strategy drafting to manuscript preparation, including retrieving references, extracting key study details, suggesting statistical tests, producing sample codes, and improving academic writing in terms of flow, organization, style, formatting, and translation. While LLMs have shown remarkable abilities in healthcare, their widespread adoption is accompanied by significant risks and challenges that must be carefully addressed. We will cover these hurdles before taking a look at the future of LLMs in healthcare and how they can be developed to meet the field's stringent requirements.