Reis Florian, Lenz Christian, Gossen Manfred, Volk Hans-Dieter, Drzeniek Norman Michael
Medical Affairs, Pfizer Pharma GmbH, Berlin, Germany.
Institute of Active Polymers, Helmholtz-Zentrum Hereon, Teltow, Germany.
JMIR Med Inform. 2024 Sep 5;12:e58478. doi: 10.2196/58478.
With the popularization of large language models (LLMs), strategies for their effective and safe usage in health care and research have become increasingly pertinent. Despite the growing interest and eagerness among health care professionals and scientists to exploit the potential of LLMs, initial attempts may yield suboptimal results due to a lack of user experience, thus complicating the integration of artificial intelligence (AI) tools into workplace routine. Focusing on scientists and health care professionals with limited LLM experience, this viewpoint article highlights and discusses 6 easy-to-implement use cases of practical relevance. These encompass customizing translations, refining text and extracting information, generating comprehensive overviews and specialized insights, compiling ideas into cohesive narratives, crafting personalized educational materials, and facilitating intellectual sparring. Additionally, we discuss general prompting strategies and precautions for the implementation of AI tools in biomedicine. Despite various hurdles and challenges, the integration of LLMs into daily routines of physicians and researchers promises heightened workplace productivity and efficiency.
随着大语言模型(LLMs)的普及,在医疗保健和研究中有效且安全地使用它们的策略变得越来越重要。尽管医疗保健专业人员和科学家对挖掘大语言模型的潜力兴趣日益浓厚且热情高涨,但由于缺乏用户经验,最初的尝试可能会产生不理想的结果,从而使将人工智能(AI)工具整合到工作流程中变得复杂。针对大语言模型经验有限的科学家和医疗保健专业人员,这篇观点文章重点介绍并讨论了6个具有实际相关性且易于实施的用例。这些用例包括定制翻译、完善文本和提取信息、生成全面概述和专业见解、将想法整理成连贯的叙述、制作个性化教育材料以及促进智力交锋。此外,我们还讨论了在生物医学中实施人工智能工具的一般提示策略和注意事项。尽管存在各种障碍和挑战,但将大语言模型整合到医生和研究人员的日常工作中有望提高工作场所的生产力和效率。