Demystifying Large Language Models for Medicine: A Primer.

ArXiv Pub Date : 2024-11-20
Qiao Jin, Nicholas Wan, Robert Leaman, Shubo Tian, Zhizheng Wang, Yifan Yang, Zifeng Wang, Guangzhi Xiong, Po-Ting Lai, Qingqing Zhu, Benjamin Hou, Maame Sarfo-Gyamfi, Gongbo Zhang, Aidan Gilson, Balu Bhasuran, Zhe He, Aidong Zhang, Jimeng Sun, Chunhua Weng, Ronald M Summers, Qingyu Chen, Yifan Peng, Zhiyong Lu
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Abstract

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.

揭开医学大型语言模型的神秘面纱:入门。
大型语言模型(LLMs)是一类变革性的人工智能工具,能够在不同的语境中生成类似人类的反应,并根据人类指令适应新任务,从而彻底改变医疗保健的各个方面。它们的潜在应用范围涵盖广泛的医疗任务,如临床文档、将患者与临床试验相匹配以及回答医疗问题。在这篇入门论文中,我们提出了一个可操作的指南,帮助医疗保健专业人员在工作中更有效地利用 LLM,并提供了一套最佳实践。该方法由几个主要阶段组成,包括制定任务、选择 LLM、提示工程、微调和部署。我们首先讨论了在确定与 LLM 核心功能相匹配的医疗保健任务以及根据所选任务和数据、性能要求和模型接口选择模型时的关键考虑因素。然后,我们回顾了使标准 LLM 适应专业医疗任务的策略,如提示工程和微调。此外,我们还讨论了部署方面的注意事项,包括监管合规性、道德准则以及对公平性和偏差的持续监控。通过提供结构化的分步方法,本教程旨在为医疗保健专业人员提供必要的工具,以便有效地将 LLM 集成到临床实践中,确保以安全、可靠和有影响力的方式应用这些强大的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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