The paper "Large Language Models for Medicine: A Survey" by Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi, Qian Liang, and Philip S. Yu provides an extensive review of large language models (LLMs) in the medical domain. The authors highlight the challenges in digital intelligence and the advancements in computational power that have enabled the integration of LLMs into various medical applications. They emphasize the importance of medical LLMs in addressing the complexities of medical information, such as understanding medical terminology, integrating diverse data sources, and providing up-to-date information. The paper outlines the development stages of LLMs, including generative models, pre-training models, and autoregressive models, and discusses their characteristics and applications. It also explores the requirements and ethical considerations for medical LLMs, such as compassionate care, interpretability, practice-oriented nature, team collaboration, and handling uncertainty and complexity. The authors review several medical LLM products, including BenTsao, Med-PaLM, PanGu drug model, HelixFold-Single, DSI-Net, MedLSAM, PubMed GPT, ChatDoctor, Med-MLLM, and PeFoMed. These models are applied in various medical fields, such as supplementary treatment and diagnosis, drug design, medical image segmentation, doctor-patient communication, and multimodal environments. The paper concludes by suggesting future research directions and technical integration to mitigate challenges and enhance the potential of medical LLMs in improving healthcare quality and efficiency.The paper "Large Language Models for Medicine: A Survey" by Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi, Qian Liang, and Philip S. Yu provides an extensive review of large language models (LLMs) in the medical domain. The authors highlight the challenges in digital intelligence and the advancements in computational power that have enabled the integration of LLMs into various medical applications. They emphasize the importance of medical LLMs in addressing the complexities of medical information, such as understanding medical terminology, integrating diverse data sources, and providing up-to-date information. The paper outlines the development stages of LLMs, including generative models, pre-training models, and autoregressive models, and discusses their characteristics and applications. It also explores the requirements and ethical considerations for medical LLMs, such as compassionate care, interpretability, practice-oriented nature, team collaboration, and handling uncertainty and complexity. The authors review several medical LLM products, including BenTsao, Med-PaLM, PanGu drug model, HelixFold-Single, DSI-Net, MedLSAM, PubMed GPT, ChatDoctor, Med-MLLM, and PeFoMed. These models are applied in various medical fields, such as supplementary treatment and diagnosis, drug design, medical image segmentation, doctor-patient communication, and multimodal environments. The paper concludes by suggesting future research directions and technical integration to mitigate challenges and enhance the potential of medical LLMs in improving healthcare quality and efficiency.