Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
Department of Medical Education, Icahn School of Medicine at Mount Sinai, NewYork, New York, USA.
Clin Infect Dis. 2024 Apr 10;78(4):860-866. doi: 10.1093/cid/ciad633.
Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural language and generate text responses to user prompts. Some approach physician performance on a range of medical challenges, leading some proponents to advocate for their potential use in clinical consultation and prompting some consternation about the future of cognitive specialties. However, LLMs currently have limitations that preclude safe clinical deployment in performing specialist consultations, including frequent confabulations, lack of contextual awareness crucial for nuanced diagnostic and treatment plans, inscrutable and unexplainable training data and methods, and propensity to recapitulate biases. Nonetheless, considering the rapid improvement in this technology, growing calls for clinical integration, and healthcare systems that chronically undervalue cognitive specialties, it is critical that infectious diseases clinicians engage with LLMs to enable informed advocacy for how they should-and shouldn't-be used to augment specialist care.
大型语言模型(LLMs)是一种通过深度学习算法训练的人工智能系统,用于处理自然语言并根据用户提示生成文本回复。一些模型在一系列医学挑战上接近医生的表现,这导致一些支持者提倡将其潜在应用于临床咨询,并引起了一些对认知专业未来的担忧。然而,LLMs 目前存在一些限制,使其无法安全地在执行专科咨询时使用,包括频繁的臆测、缺乏对细微诊断和治疗计划至关重要的上下文意识、难以理解和无法解释的训练数据和方法,以及容易重现偏见的倾向。尽管如此,考虑到这项技术的快速改进、越来越多的临床整合需求,以及医疗保健系统长期低估认知专业的价值,传染病临床医生必须与 LLM 进行接触,以便为他们应该——以及不应该——如何被用来增强专科护理提供明智的建议。