X-Ray

Catch up on the top AI-related news and research from the past month.

Review the case study and test your knowledge to make the correct diagnosis.

Catch up on the top radiology content of the past week.

Is there a certain line of self-preservation in radiology reporting for findings and impressions?

Review the case study and test your knowledge to make the correct diagnosis.

The artificial intelligence (AI)-enabled software, which has a documented 91 percent sensitivity rate for detecting pediatric fractures, is reportedly the first AI fracture detection modality to receive FDA 510(k) clearance for use in the pediatric population.

Catch up on the top radiology content of the past week.

In a study of over 1,500 patients, researchers found that an emerging artificial intelligence (AI) modality had significantly higher sensitivity rates for abnormal posteroanterior chest radiographs and critical finding radiographs than radiology reports.

The new launches include the 80/160-slice computed tomography (CT) scanner Aquilion Serve, which allows simultaneous previews of axial, lateral and AP views, and Celex, a multipurpose X-ray system that offers automated and customizable features to help maximize workflow efficiencies.

From pagers, transcriptionists, and low-tech X-rays to teleradiology, advanced imaging and artificial intelligence (AI), this author considers the evolution of radiology over three decades.

Catch up on the top AI-related news and research in radiology over the past month.

Can more easygoing radiologists coexist with productivity-driven colleagues?

When you are asked about your occupation, how do you respond?

Catch up on the top radiology content of the past week.

In newly published research, researchers found that an artificial intelligence (AI) computer-aided detection (CAD) system was more than twice as likely as non-AI assessment to diagnose actionable lung nodules on chest X-rays.

From enhanced image quality and workflow efficiencies to an improved patient experience and potential synergies wih enterprise cloud services, artificial intelligence continues to redefine possibilities in radiology.

In an external validation data set for a deep learning bone-suppressed (DLBS) model, researchers found that adjunctive use of the DLBS model led to a nearly 15 percent increase in sensitivity for detecting pulmonary nodules on chest X-rays in comparison to radiologist assessment.

When you’re asked to review an X-ray for a patient who already had follow-up imaging, do you consider the results of follow-up imaging or evaluate the X-ray with fresh eyes?

Catch up on the top AI-related news and research of the past month.

Catch up on the top radiology content of the past week.

Noting the significant administrative fees for the Independent Dispute Resolution (IDR) process of the No Surprises Act and onerous restrictions that have led to a nearly “non-existent” use of batching of disputed claims in radiology, the American College of Radiology (ACR) has sent formal recommendations to the United States Departments of Health and Human Services, Labor, and Treasury for addressing these issues.

In a study involving patients who presented to emergency departments with acute chest pain, a deep learning model demonstrated significantly improved prediction of aortic dissection and all-cause mortality and indicated that additional pulmonary and cardiovascular testing could be deferred in seven times as many patients as suggested by conventional risk factors and testing measures.

Review the case study and test your knowledge to make the correct diagnosis.

Acknowledging the repetitive reality that accompanies productivity incentives and seemingly extraneous verbiage to satisfy certain insurance requirements in radiology, this author has developed an appreciation for filler-free brevity and quiet.

For system errors and failures in radiology, are we prone to a satisfaction of search that prevents us from addressing deeper issues?