We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




AI Helps Physicians Achieve Higher Diagnostic Accuracy in Interpreting Chest X-Rays

By MedImaging International staff writers
Posted on 12 Jan 2024
Image: Artificial intelligence improves detection during chest X-ray interpretation (Photo courtesy of 123RF)
Image: Artificial intelligence improves detection during chest X-ray interpretation (Photo courtesy of 123RF)

Artificial intelligence (AI)–assisted diagnosis imparts high accuracy to chest radiography (CXR) interpretation; however, its benefit for non-radiologist physicians in detecting lung lesions on CXR remains unclear. Now, a new study has found that AI-assisted CXR interpretation improves the diagnostic performance of non-radiologist physicians in detecting abnormal lung findings.

The study was conducted by researchers at the Konyang University School of Medicine (Daejeon, Korea) to investigate whether AI assistance improves the diagnostic performance of physicians for CXR interpretation and affects their clinical decisions in clinical practice. The researchers randomly allocated eligible patients who visited an outpatient clinic to the intervention (with AI-assisted interpretation) and control (without AI-assisted interpretation) groups. Lung lesions on CXR were recorded by seven non-radiologists with or without AI assistance. The reference standard for lung lesions was established by three radiologists. The primary and secondary endpoints were the physicians’ diagnostic accuracy and clinical decision, respectively.

Between October 2020 and May 2021, 162 and 161 patients were assigned to the intervention and control groups, respectively. The area under the receiver operating characteristic curve was significantly larger in the intervention group than in the control group for the CXR level and lung lesion level. The intervention group had higher sensitivity in terms of both CXR and lung lesion level and a lower false referral rate for the lung lesion level. AI-assisted CXR interpretation did not affect the physicians’ clinical decisions. Based on their study results, the researchers concluded that AI-assisted CXR interpretation improves the diagnostic performance of non-radiologist physicians in detecting abnormal lung findings.

“Physicians showed a better performance in [chest radiography] interpretation with AI assistance than without it,” stated Hyun Woo Lee, MD. “AI assistance allowed physicians to find more lung lesions.”

Related Links:
Konyang University School of Medicine

High-Precision QA Tool
DEXA Phantom
Mammo DR Retrofit Solution
DR Retrofit Mammography
40/80-Slice CT System
uCT 528
Ultrasound-Guided Biopsy & Visualization Tools
Endoscopic Ultrasound (EUS) Guided Devices

Channels

Nuclear Medicine

view channel
Image: The new tracer, 64Cu-NOTA-EV-F(ab′)2​, targets nectin-4, a protein strongly linked to tumor growth in both TNBC and UBC cancer types. (Wenpeng Huang et al., DOI: 10.2967/jnumed.125.270132)

PET Tracer Enables Same-Day Imaging of Triple-Negative Breast and Urothelial Cancers

Triple-negative breast cancer (TNBC) and urothelial bladder carcinoma (UBC) are aggressive cancers often diagnosed at advanced stages, leaving limited time for effective treatment decisions.... Read more

General/Advanced Imaging

view channel
Image: Concept of the photo-thermoresponsive SCNPs (J F Thümmler et al., Commun Chem (2025). DOI: 10.1038/s42004-025-01518-x)

New Ultrasmall, Light-Sensitive Nanoparticles Could Serve as Contrast Agents

Medical imaging technologies face ongoing challenges in capturing accurate, detailed views of internal processes, especially in conditions like cancer, where tracking disease development and treatment... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.