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循证医学影像学人工智能

Evidence-Based Artificial Intelligence in Medical Imaging.

机构信息

Department of Psychiatry and Behavioural Neurosciences, McMaster University; Department of Psychiatry, University of Toronto; St. Joseph's Healthcare, West 5th Campus, 100 West 5th Street, Hamilton, Ontario L8N 3K7, Canada.

Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

出版信息

PET Clin. 2022 Jan;17(1):51-55. doi: 10.1016/j.cpet.2021.09.005.

Abstract

Artificial intelligence (AI) in medical imaging is in its infancy. However, ongoing advances in hardware and software as well as increasing access to ever-expanding datasets for training, validation, and testing purposes are likely to make AI an increasingly prevalent and powerful tool. Of course issues, such as the need to protect the privacy of sensitive health data, remain; nevertheless, it is likely the average imager will need to develop an evidence-based approach to assessing AI in medical imaging. We hope this article will provide insight into just how this can be conducted by applying 5 simple questions, specifically: (1) Who was in the training sample, (2) How was the model trained, (3) How reliable is the algorithm, (4) How was the model validated, and (5) How useable is the algorithm.

摘要

人工智能(AI)在医学影像领域仍处于起步阶段。然而,硬件和软件的不断进步,以及为培训、验证和测试目的而获得的不断扩展的数据集,都可能使 AI 成为一种越来越普及和强大的工具。当然,一些问题仍然存在,例如需要保护敏感健康数据的隐私;然而,很可能普通影像医师需要制定一种基于证据的方法来评估医学影像中的 AI。我们希望本文通过应用 5 个简单的问题,为如何进行这一评估提供一些见解,具体问题如下:(1)训练样本中有谁,(2)模型是如何训练的,(3)算法的可靠性如何,(4)模型是如何验证的,以及(5)算法的可用性如何。

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