Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices. This approach consists of several main phases, including formulating the task, choosing LLMs, prompt engineering, fine-tuning, and deployment. We start with the discussion of critical considerations in identifying healthcare tasks that align with the core capabilities of LLMs and selecting models based on the selected task and data, performance requirements, and model interface. We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks. Deployment considerations, including regulatory compliance, ethical guidelines, and continuous monitoring for fairness and bias, are also discussed. By providing a structured step-by-step methodology, this tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.

翻译：大型语言模型（LLMs）是一类变革性的人工智能工具，能够通过在不同场景中生成类人响应并遵循人类指令适应新任务，从而革新医疗保健的各个方面。其潜在应用涵盖广泛的医疗任务，例如临床文档记录、患者与临床试验匹配以及医学问题解答。在本入门指南中，我们提出一套可操作的指导原则和最佳实践方案，以帮助医疗专业人员更高效地利用LLMs开展工作。该方法包含若干主要阶段：任务定义、模型选择、提示工程、微调及部署。我们首先探讨识别与LLMs核心能力相匹配的医疗任务时需考虑的关键要素，以及如何根据选定任务与数据、性能需求和模型接口来选择模型。随后，我们综述了提示工程与微调等策略，以使通用LLMs适配专业医疗任务。文中亦讨论了部署考量因素，包括法规合规性、伦理准则以及对公平性与偏见的持续监测。通过提供结构化的分步方法，本指南旨在为医疗专业人员提供将LLMs有效整合到临床实践所需的工具，确保这些强大技术能以安全、可靠且具有影响力的方式得以应用。