Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial-Reference-Cited by-同舟云学术

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial

Published:2023 Issue:3 Volume:24 Page:259
ISSN:1229-6929
Container-title:Korean Journal of Radiology
language:en
Short-container-title:Korean J Radiol

Author:

Hwang Eui Jin¹^ORCID,Goo Jin Mo¹²³^ORCID,Nam Ju Gang¹^ORCID,Park Chang Min¹²³^ORCID,Hong Ki Jeong⁴^ORCID,Kim Ki Hong⁴^ORCID

Affiliation:

1. Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

2. Cancer Research Institute, Seoul National University, Seoul, Korea.

3. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.

4. Department of Emergency Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

Funder

Korea Health Industry Development Institute

Ministry of Health and Welfare

Publisher

XMLink

Subject

Radiology, Nuclear Medicine and imaging

Link

https://kjronline.org/pdf/10.3348/kjr.2022.0651

Reference28 articles.

1. ACR Appropriateness Criteria Acute Nonspecific Chest Pain—Low Probability of Coronary Artery Disease

2. ACR Appropriateness Criteria® Acute Respiratory Illness in Immunocompromised Patients

3. ACR Appropriateness Criteria® Hemoptysis

4. National Hospital Ambulatory Medical Care Survey: 2018 emergency department summary tables. Centers for Disease Control and Prevention.com Web site. 2021. Accessed August 30, 2022. https://www.cdc.gov/nchs/data/nhamcs/web_tables/2018-ed-web-tables-508.pdf

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Performing a Research Study Using Open-Source Deep Learning Models;Korean Journal of Radiology;2024

2. Apport de l’intelligence artificielle dans le post-traitement de l’imagerie thoracique;Revue des Maladies Respiratoires;2023-12

3. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia;Korean Journal of Radiology;2023