This paper provides a detailed examination of the advancements and applications of large language models in the healthcare sector, with a particular emphasis on clinical applications. The study traces the evolution of LLMs from their foundational technologies to the latest developments in domain-specific models and multimodal integration. It explores the technical progression from encoder-based models requiring fine-tuning to sophisticated approaches that integrate textual, visual, and auditory data, thereby facilitating comprehensive AI solutions in healthcare. The paper discusses both the opportunities these technologies present for enhancing clinical efficiency and the challenges they pose in terms of ethics, data privacy, and implementation. Additionally, it critically evaluates the deployment strategies of LLMs, emphasizing the necessity of open-source models to ensure data privacy and adaptability within healthcare environments. Future research directions are proposed, focusing on empirical studies to evaluate the real-world efficacy of LLMs in healthcare and the development of open datasets for further research. This review aims to provide a comprehensive resource for both newcomers and multidisciplinary researchers interested in the intersection of AI and healthcare.

本文解决了大型语言模型在医疗领域应用中的挑战和机遇，特别是在临床中的应用。通过对基础技术及其最新发展进行详细讨论，研究提出了整合文本、视觉和听觉数据的新方法，从而提供全面的AI解决方案。研究发现，这些技术有潜力提升临床效率，但也面临伦理和数据隐私等挑战，强调了开源模型在保障数据隐私和适应性方面的重要性。

临床洞察：医学领域语言模型的综合评审