Deployment of Artificial Intelligence in Radiology: Strategies for Success

被引：3

作者：

Jiang, Sirui ^{[1
]}

Bukhari, Syed M. A. ^{[1
]}

Krishnan, Arjun ^{[2
]}

Bera, Kaustav ^{[1
]}

Sharma, Avishkar ^{[3
]}

Caovan, Danielle ^{[1
]}

Rosipko, Beverly ^{[1
]}

Gupta, Amit ^{[1
]}

机构：

[1] Univ Hosp Cleveland Med Ctr, Dept Radiol, 11100 Euclid Ave, Cleveland, OH 44106 USA

[2] Cleveland State Univ, Dept Biol, Cleveland, OH USA

[3] Jefferson Einstein Philadelphia Hosp, Dept Radiol, Philadelphia, PA USA

来源：

AMERICAN JOURNAL OF ROENTGENOLOGY | 2025年 / 224卷 / 02期

关键词：

artificial intelligence; clinical challenges; integration; radiology; validation; EXTERNAL VALIDITY; VALIDATION; MODELS;

D O I：

10.2214/AJR.24.31898

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Radiology, as a highly technical and information-rich medical specialty, is well suited for artificial intelligence (AI) product development, and many U.S. FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.

引用

页数：11

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