Large language models (LLMs) have emerged as transformative tools in healthcare, leveraging advanced natural language processing to enhance clinical workflows, patient communication, and medical education. This survey paper provides a comprehensive analysis of LLMs’ applications, including named entity recognition, clinical decision support, and patient-friendly report generation, highlighting their ability to process unstructured medical data such as electronic health records and biomedical literature. Domain-specific models like BioBERT and ClinicalBERT, alongside generative models like GPT-3 and Med-PaLM, demonstrate superior performance in tasks requiring medical context, achieving high accuracy in predictive analytics and question answering. However, significant challenges impede their widespread adoption, including computational intensity, data biases, privacy concerns, and regulatory uncertainties. Ethical issues, such as perpetuating healthcare disparities, and technical limitations, like sensitivity to noisy data, necessitate innovative solutions like federated learning, differential privacy, and explainable AI. The paper also explores multimodal LLMs integrating text with imaging or genomic data, which promise holistic diagnostic capabilities. Future directions focus on developing lightweight, interpretable models and standardized frameworks to ensure equitable and safe deployment. By synthesizing current advancements, methodologies, and obstacles, this survey underscores the transformative potential of LLMs in healthcare while advocating for collaborative efforts among researchers, clinicians, and policymakers to address challenges and realize their full impact on improving patient outcomes and healthcare efficiency.