医学
工作流程
经济短缺
临床实习
医学物理学
人工智能应用
放射科
多样性(控制论)
人工智能
物理疗法
计算机科学
语言学
哲学
数据库
政府(语言学)
作者
Ali Guermazi,Patrick Omoumi,Mickaël Tordjman,Jan Fritz,Richard Kijowski,Nor-Eddine Regnard,John A. Carrino,Charles E. Kahn,Florian Knöll,Daniel Rueckert,Frank W. Roemer,Daichi Hayashi
出处
期刊:Radiology
[Radiological Society of North America]
日期:2024-01-01
卷期号:310 (1)
被引量:24
标识
DOI:10.1148/radiol.230764
摘要
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities. © RSNA, 2024 Supplemental material is available for this article. See also the review "Present and Future Innovations in AI and Cardiac MRI" by Morales et al in this issue. An earlier incorrect version appeared online. This article was corrected on January 19, 2024.
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