Anesthesiology, University of California San Diego, La Jolla, California, USA
Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, Palo Alto, California, USA.
Reg Anesth Pain Med. 2023 Nov;48(11):575-577. doi: 10.1136/rapm-2023-104637. Epub 2023 Jun 19.
Interest in natural language processing, specifically large language models, for clinical applications has exploded in a matter of several months since the introduction of ChatGPT. Large language models are powerful and impressive. It is important that we understand the strengths and limitations of this rapidly evolving technology so that we can brainstorm its future potential in perioperative medicine. In this daring discourse, we discuss the issues with these large language models and how we should proactively think about how to leverage these models into practice to improve patient care, rather than worry that it may take over clinical decision-making. We review three potential major areas in which it may be used to benefit perioperative medicine: (1) clinical decision support and surveillance tools, (2) improved aggregation and analysis of research data related to large retrospective studies and application in predictive modeling, and (3) optimized documentation for quality measurement, monitoring and billing compliance. These large language models are here to stay and, as perioperative providers, we can either adapt to this technology or be curtailed by those who learn to use it well.
自 ChatGPT 问世以来,人们对自然语言处理(尤其是大型语言模型)在临床应用中的兴趣在短短几个月内迅速爆发。大型语言模型功能强大,令人印象深刻。了解这项快速发展的技术的优势和局限性非常重要,以便我们能够集思广益,探讨其在围手术期医学中的未来潜力。在这篇大胆的论述中,我们讨论了这些大型语言模型存在的问题,以及我们应该如何积极思考如何将这些模型应用于实践,以改善患者护理,而不是担心它们可能会接管临床决策。我们回顾了它可能在三个主要领域用于受益于围手术期医学:(1)临床决策支持和监测工具,(2)改进与大型回顾性研究相关的研究数据的聚合和分析,并应用于预测建模,(3)优化文档质量测量、监测和计费合规性。这些大型语言模型已经存在,作为围手术期提供者,我们可以适应这项技术,或者被那些善于使用它的人所限制。