Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL; Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL.
Semin Roentgenol. 2023 Apr;58(2):208-213. doi: 10.1053/j.ro.2023.02.006. Epub 2023 Mar 23.
There is a steadily increasing number of artificial intelligence (AI) tools available and cleared for use in clinical radiological practice. Radiologists will increasingly be faced with options provided by other radiologist colleagues, clinician colleagues, vendors, or other professionals for obtaining and deploying AI algorithms in clinical practice. It is important that radiologists are familiar with basic and practical aspects that need to be considered when assessing an AI tool for use in their practice, so that resources are properly allocated and there is an appropriate return on investment through enhancements in patient quality of care, safety, and/or process efficiency. In this review, we will discuss a potential approach for AI software assessment and practical points that should be considered when considering the acquisition and deployment of an AI tool in the radiology department.
目前有越来越多的人工智能（AI）工具可用于临床放射学实践，并已获得批准。放射科医生将越来越多地面临其他放射科医生同事、临床医生同事、供应商或其他专业人士提供的选择，以在临床实践中获取和部署 AI 算法。重要的是，放射科医生应该熟悉在评估 AI 工具在其实践中的使用时需要考虑的基本和实际方面，以便通过提高患者护理质量、安全性和/或流程效率来合理分配资源并获得适当的投资回报。在这篇综述中，我们将讨论一种潜在的 AI 软件评估方法，并讨论在放射科考虑获取和部署 AI 工具时应考虑的实际问题。
