Recent advances in large language models (LLMs) have significant potential for healthcare applications. This paper explores the capabilities and challenges of LLMs in the medical field. It discusses major LLM architectures such as GPT, Bloom, and LLaMA, which have billions of parameters and demonstrate strong language understanding and generation abilities. The paper also examines recent trends in medical datasets used to train these models, classifying them by size, source, and subject matter. It highlights the potential of LLMs to improve patient care, accelerate medical research, and optimize healthcare systems through automation. Key challenges, including ethical issues around privacy, confidentiality, and algorithmic bias, are discussed. The paper proposes a discussion of the capabilities of new LLM generations and their limitations in healthcare applications. It also outlines the structure of the paper, including a brief history of LLMs, the transformer architecture, and the practical applications of LLMs in healthcare. The paper emphasizes the importance of rigorous evaluation to ensure the safe and equitable use of LLMs in healthcare. It also discusses the ethical challenges of using AI in healthcare, including patient safety, bias, and privacy. The paper concludes with a discussion of the potential of LLMs to transform medicine and health research.Recent advances in large language models (LLMs) have significant potential for healthcare applications. This paper explores the capabilities and challenges of LLMs in the medical field. It discusses major LLM architectures such as GPT, Bloom, and LLaMA, which have billions of parameters and demonstrate strong language understanding and generation abilities. The paper also examines recent trends in medical datasets used to train these models, classifying them by size, source, and subject matter. It highlights the potential of LLMs to improve patient care, accelerate medical research, and optimize healthcare systems through automation. Key challenges, including ethical issues around privacy, confidentiality, and algorithmic bias, are discussed. The paper proposes a discussion of the capabilities of new LLM generations and their limitations in healthcare applications. It also outlines the structure of the paper, including a brief history of LLMs, the transformer architecture, and the practical applications of LLMs in healthcare. The paper emphasizes the importance of rigorous evaluation to ensure the safe and equitable use of LLMs in healthcare. It also discusses the ethical challenges of using AI in healthcare, including patient safety, bias, and privacy. The paper concludes with a discussion of the potential of LLMs to transform medicine and health research.