ChatGPT and Large Language Models in Radiology: Perspectives From the Field

被引：0

作者：

Bahl, Manisha ^{[1
]}

Balthazar, Patricia ^{[2
]}

Davis, Melissa A. ^{[3
]}

Makary, Mina S. ^{[4
]}

Tirumani, Sree Harsha ^{[5
]}

Whitlow, Christopher T. ^{[6
,7
,8
,9
]}

机构：

[1] Harvard Med Sch, Dept Radiol, Massachusetts Gen Hosp, Boston, MA 02115 USA

[2] Emory Univ, Sch Med, Dept Radiol & Imaging Sci, Atlanta, GA USA

[3] Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT USA

[4] Ohio State Univ, Med Ctr, Dept Radiol, Columbus, OH USA

[5] Case Western Reserve Univ, UH Cleveland Med Ctr, Dept Radiol, Cleveland, OH USA

[6] Wake Forest Univ, Bowman Gray Sch Med, Dept Radiol, Winston Salem, NC USA

[7] Wake Forest Univ, Bowman Gray Sch Med, Dept Biomed Engn, Winston Salem, NC USA

[8] Wake Forest Univ, Bowman Gray Sch Med, Dept Biostat & Data Sci, Winston Salem, NC USA

[9] Atrium Hlth, Winston Salem, NC USA

来源：

AMERICAN JOURNAL OF ROENTGENOLOGY | 2024年 / 223卷 / 04期

关键词：

artificial intelligence; ChatGPT; large; language model;

D O I：

10.2214/AJR.24.32022

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：

Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being recognized as tools with the potential to transform many industries, including health care. Implementation and use of these tools among radiologists is likely variable, driven by radiology practice and institutional factors. Radiologists from various practices were asked about their perspectives on generative AI and LLMs in radiology.

引用

页数：5

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