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.

MedImaging

Download Mobile App
Recent News Radiography MRI Ultrasound Nuclear Medicine General/Advanced Imaging Imaging IT Industry News

First Autonomous AI Medical Imaging Application Reads Chest X-Rays without Radiologist Involvement

By MedImaging International staff writers
Posted on 01 Apr 2022
Print article
Image: ChestLink is the first fully autonomous AI medical imaging product (Photo courtesy of Unsplash)
Image: ChestLink is the first fully autonomous AI medical imaging product (Photo courtesy of Unsplash)

AI autonomy in medical imaging is not driven by the technology, but by the current systematic healthcare shortcomings the platforms aim to address, namely the understaffed radiology departments in developed countries. Now, an AI imaging application that autonomously reports on chest X-rays featuring no abnormalities without any involvement from a radiologist could reduce radiologist workload and enable them to focus on cases with pathologies.

Oxipit’s (Vilnius, Lithuania) ChestLink autonomous AI imaging suite is the first fully autonomous AI medical imaging product with a CE mark. ChestLink identifies CXRs with no abnormality and produces finalized patient reports without any intervention from the radiologist. By autonomously reporting on CXRs with no abnormalities where it is highly certain of the results, ChestLink may automate from 15% to 40% of daily reporting, freeing up radiologists to report on cases that feature pathologies.

Prior to certification, ChestLink has been operating in a supervised reporting setting in multiple pilot locations for more than a year, processing more than 500,000 real-world chest X-ray images. For operational oversight ChestLink application provides an analytics page with real-time updates and daily summaries on what cases were autonomously reported on, allowing to quickly trace the steps of application decisions. Prior to autonomous operations, ChestLink deployments start with a retrospective imaging audit. Retrospective analysis helps to identify what part of studies at the medical institution can be successfully automated. The operations then move into a supervised setting, where ChestLink reports are validated by the Oxipit medical staff and radiologists at the medical institution. Only after completing the initial stages, the application can start to report autonomously.

“ChestLink ushers in the era of AI autonomy in healthcare - something we have been promised by medical futurists and technology experts. It presents the first case where a medical diagnostic evaluation will be carried out solely by an artificial intelligence application. ChestLink showcases the future of healthcare diagnostics, where AI operates as an integral part of the clinical workflow,” said CEO of Oxipit Gediminas Peksys.

Related Links:
Oxipit 

Gold Member
Ultrasound System
FUTUS LE
Gold Member
Electrode Solution and Skin Prep
Signaspray
Body Array Coil
12-Channel Body Array Coil 1.5 / 3.0 T
X-Ray Generator
RF Series

Print article
Radcal

Channels

MRI

view channel
Image: PET/MRI can accurately classify prostate cancer patients (Photo courtesy of 123RF)

PET/MRI Improves Diagnostic Accuracy for Prostate Cancer Patients

The Prostate Imaging Reporting and Data System (PI-RADS) is a five-point scale to assess potential prostate cancer in MR images. PI-RADS category 3 which offers an unclear suggestion of clinically significant... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... 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