Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency-Reference-Cited by-同舟云学术

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Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Published:2022-08-31 Issue:8 Volume:5 Page:e2229289
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Ahn Jong Seok¹,Ebrahimian Shadi²³,McDermott Shaunagh²,Lee Sanghyup¹,Naccarato Laura²,Di Capua John F.²,Wu Markus Y.²,Zhang Eric W.²,Muse Victorine²,Miller Benjamin²⁴,Sabzalipour Farid²⁴,Bizzo Bernardo C.²⁴,Dreyer Keith J.²⁴,Kaviani Parisa²,Digumarthy Subba R.²,Kalra Mannudeep K.²⁴

Affiliation:

1. Lunit, Inc, Seoul, South Korea

2. Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

3. Internal Medicine, Icahn School of Medicine at Mount Sinai, Elmhurst Hospital Center, Elmhurst, New York

4. Data Science Office, Mass General Brigham, Boston, Massachusetts

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2795798/ahn_2022_oi_220833_1661274150.26442.pdf

Reference41 articles.

1. ACR Appropriateness Criteria® routine chest radiography.;McComb;J Thorac Imaging,2016

2. Patient exposure from radiologic and nuclear medicine procedures in the United States: procedure volume and effective dose for the period 2006-2016.;Mettler;Radiology,2020

3. Pitfalls in chest radiographic interpretation: blind spots.;de Groot;Semin Roentgenol,2015

4. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect.;Austin;Radiology,1992

5. Intraobserver and interobserver agreement of the interpretation of pediatric chest radiographs.;Johnson;Emerg Radiol,2010

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