The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 North Caroline Street, MD, 21287, Baltimore, USA.
Transcarent, Inc., 2 S Park St., FL. 1, San Francisco, CA, 94107, USA.
J Digit Imaging. 2022 Jun;35(3):534-537. doi: 10.1007/s10278-022-00598-8. Epub 2022 Feb 15.
We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist's report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists.
我们是众多认为人工智能不会取代从业者,并且在诊断放射学中作为辅助手段最有价值的人之一。我们建议采用一种不同的方法来利用这项技术,这可能有助于即使是可能不愿意采用人工智能的放射科医生。一种利用人工智能的新方法结合了计算机视觉和自然语言处理,以便在后台环境中运行,监测关键护理差距。这种人工智能质量工作流程使用视觉分类器来预测感兴趣的发现(例如肺结节)的可能性,然后利用自然语言处理来审查放射科医生的报告,识别影像学和文档之间的差异。将人工智能的预测与人工智能在计算机辅助检测决策中的背景下的自然语言处理报告提取进行比较,可能会带来许多潜在的好处,包括简化工作流程、提高检测质量、提供一种思考人工智能的替代方法,甚至可能防止医疗事故。在这里,我们考虑人工智能作为放射科医生最终质量保证的早期迹象。