LLMs have emerged as powerful tools in healthcare, offering transformative solutions to improve patient care, streamline clinical workflows, and enhance medical research. These models, built upon advanced NLP techniques and trained on vast amounts of text data, can understand, generate, and analyze human language with unprecedented accuracy and complexity. This paper provides a comprehensive overview of LLMs in healthcare, covering their fundamentals, applications, advantages, challenges, and future directions. We discuss the evolution and development of LLMs, their key components and architectures, and the training and fine-tuning processes involved. Furthermore, we explore many applications of LLMs in healthcare, including clinical documentation, medical literature analysis, diagnostic assistance, and patient engagement. We also examine the advantages of LLMs in improving healthcare delivery, such as enhancing clinical decision-making, reducing administrative burden, and facilitating patient-provider communication. However, adopting LLMs in healthcare has challenges, including ethical and privacy considerations, technical limitations, and bias mitigation strategies. Through case studies and use cases, we highlight successful implementations of LLMs in healthcare settings and discuss lessons learned and best practices. Finally, we provide recommendations and guidelines for researchers, practitioners, and policymakers to harness the full potential of LLMs while ensuring ethical and responsible use. This paper underscores the significance of LLMs in shaping the future of healthcare and calls for continued research and innovation in this rapidly evolving field.