This comprehensive survey evaluates the application and evaluation of Large Language Models (LLMs) in the medical industry. It highlights the transformative potential of LLMs in various healthcare settings, emphasizing the need for specialized evaluation frameworks to ensure their effective and ethical deployment. The survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness. Key sections include: 1. **Introduction & Background**: Discusses the evolution of LLMs, from the Transformer architecture to advanced models like GPT-4, and their applications in various industries. 2. **Taxonomy and Structure of the Survey**: Outlines the structure of the review, focusing on application fields, evaluation methods, and benchmarks. 3. **Current State of LLM Application Evaluations in the Medical Field**: Provides detailed evaluations of LLMs in different medical departments and specific diseases, including clinical applications, medical text data processing, research, education, and public awareness. 4. **Evaluation Methods and Metrics**: Explores the methodologies used in evaluating LLMs, including models, evaluators, comparative experiments, and evaluation indicators. 5. **Benchmarks and Datasets**: Describes the benchmarks and datasets used in evaluations, such as those for named entity recognition, relation extraction, and information retrieval. The survey aims to equip healthcare professionals, researchers, and policymakers with a comprehensive understanding of the strengths and limitations of LLMs in medical applications, guiding responsible development and deployment while maintaining ethical standards.This comprehensive survey evaluates the application and evaluation of Large Language Models (LLMs) in the medical industry. It highlights the transformative potential of LLMs in various healthcare settings, emphasizing the need for specialized evaluation frameworks to ensure their effective and ethical deployment. The survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness. Key sections include: 1. **Introduction & Background**: Discusses the evolution of LLMs, from the Transformer architecture to advanced models like GPT-4, and their applications in various industries. 2. **Taxonomy and Structure of the Survey**: Outlines the structure of the review, focusing on application fields, evaluation methods, and benchmarks. 3. **Current State of LLM Application Evaluations in the Medical Field**: Provides detailed evaluations of LLMs in different medical departments and specific diseases, including clinical applications, medical text data processing, research, education, and public awareness. 4. **Evaluation Methods and Metrics**: Explores the methodologies used in evaluating LLMs, including models, evaluators, comparative experiments, and evaluation indicators. 5. **Benchmarks and Datasets**: Describes the benchmarks and datasets used in evaluations, such as those for named entity recognition, relation extraction, and information retrieval. The survey aims to equip healthcare professionals, researchers, and policymakers with a comprehensive understanding of the strengths and limitations of LLMs in medical applications, guiding responsible development and deployment while maintaining ethical standards.