With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This has sparked significant interest in applying LLMs to enhance various aspects of healthcare, ranging from medical education to clinical decision support. However, medicine involves multifaceted data modalities and nuanced reasoning skills, presenting challenges for integrating LLMs. This paper provides a comprehensive review on the applications and implications of LLMs in medicine. It begins by examining the fundamental applications of general-purpose and specialized LLMs, demonstrating their utilities in knowledge retrieval, research support, clinical workflow automation, and diagnostic assistance. Recognizing the inherent multimodality of medicine, the review then focuses on multimodal LLMs, investigating their ability to process diverse data types like medical imaging and EHRs to augment diagnostic accuracy. To address LLMs' limitations regarding personalization and complex clinical reasoning, the paper explores the emerging development of LLM-powered autonomous agents for healthcare. Furthermore, it summarizes the evaluation methodologies for assessing LLMs' reliability and safety in medical contexts. Overall, this review offers an extensive analysis on the transformative potential of LLMs in modern medicine. It also highlights the pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice. Visit https://github.com/mingze-yuan/Awesome-LLM-Healthcare for an accompanying GitHub repository containing latest papers.

翻译：随着人工智能的快速发展，大型语言模型（LLMs）在模拟人类级语言理解和推理方面展现出令人瞩目的能力。这引发了将LLMs应用于提升医疗健康各领域——从医学教育到临床决策支持——的浓厚兴趣。然而，医学涉及多模态数据形式和精细的推理技能，为整合LLMs带来了挑战。本文系统综述了LLMs在医学领域的应用与影响。首先探讨通用型与专用型LLMs的基础应用，展示其在知识检索、研究支持、临床工作流自动化和诊断辅助中的效用。在认识到医学固有的多模态特性后，综述聚焦于多模态LLMs，考察其处理医学影像、电子健康记录等多样化数据类型以提升诊断准确性的能力。针对LLMs在个性化和复杂临床推理方面的局限性，本文进一步探究了由LLM驱动的医疗自主智能体的前沿发展。此外，文章总结了评估LLMs在医学场景中可靠性与安全性的评价方法。总体而言，本综述全面分析了LLMs在现代医学中的变革潜力，并强调了在有效整合至临床实践前进行持续优化与伦理监管的关键必要性。相关配套GitHub资源库（https://github.com/mingze-yuan/Awesome-LLM-Healthcare）收录了最新论文。