Li Ron C, Asch Steven M, Shah Nigam H
Division of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA.
Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA USA.
NPJ Digit Med. 2020 Aug 21;3:107. doi: 10.1038/s41746-020-00318-y. eCollection 2020.
Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real world setting has yet to be realized. In order to safely and effectively bring AI into use in healthcare, there needs to be a concerted effort around not just the creation, but also the delivery of AI. This AI "delivery science" will require a broader set of tools, such as design thinking, process improvement, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable. The careful design, implementation, and evaluation of these AI enabled systems will be important in the effort to understand how AI can improve healthcare.
人工智能(AI)在医疗保健领域引发了极大的轰动,这主要是由日益精确的机器学习模型的出现所推动的。然而,人工智能在现实世界中为患者护理提供可扩展且持续价值的前景尚未实现。为了在医疗保健中安全有效地使用人工智能,不仅需要围绕人工智能的创建,还需要围绕其交付齐心协力。这种人工智能“交付科学”将需要更广泛的一系列工具,如设计思维、流程改进和实施科学,以及对人工智能在实践中的样子有更广泛的定义,这不仅包括机器学习模型及其预测,还包括它们所促成的新的护理交付系统。对这些人工智能支持的系统进行精心设计、实施和评估,对于理解人工智能如何改善医疗保健至关重要。