Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, California.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1309-1317. doi: 10.1016/j.jacr.2019.05.036.
Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.
人工智能 (AI) 方法的快速技术进步推动了越来越多的公司推出的决策工具的爆炸式增长。人工智能的发展主要由计算机科学家、信息学家、工程师和商人推动,放射科医生的直接参与要少得多。放射科医生对 AI 的参与主要限于使他们熟悉这些工具和有前景的结果的教育工作,但帮助他们决定在实践中使用哪些 AI 工具以及如何量化其价值的技术并未得到解决。本文重点介绍放射科医生在医学影像 AI 中的作用,并提出了一些具体的方法,通过这些方法放射科医生可以(1)考虑在特定临床用例中 AI 工具的临床需求,(2)对他们正在考虑在实践中采用的 AI 工具进行正式评估,以及(3)保持他们的专业知识并防止过度依赖技术的陷阱。