Recently published research projected that 103,000 future cases of radiation-induced cancer would result from 93 million computed tomography (CT) exams performed in the United States in 2023.

In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.

The Nanox.ARC X system reportedly provides enhanced 3D imaging for a variety of indications, ranging from pulmonary imaging and intra-abdominal views to musculoskeletal assessment.

Seniors who had at least one prior screening mammography exam in the five years prior to breast cancer diagnosis were over a third less likely to die from breast cancer, according to new research.

Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.

The additional FDA 510(k) clearance for the AI-powered cmAngio platform covers use of the software for GE HealthCare mammography systems.

An emerging nomogram model for intra-tumoral heterogeneity quantification with breast MRI demonstrated an average 85 percent sensitivity in external validation testing for predicting pathologic complete response to neoadjuvant chemotherapy for breast cancer.

Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.

Recent research has demonstrated that the AI software HeartFocus enabled novice health-care providers to achieve greater than 85 percent agreement with expert sonographers in assessing echocardiographic parameters.

In a recent interview, Rebecca Smith-Bindman, M.D., offered key insights on new research examining the link between computed tomography scans and projected future cases of radiation-induced cancer.

In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).

Some abbreviations for radiology transcriptions defy reasonable explanation but they continue to persist on multiple voice recognition platforms.

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

For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.

The Bunkerhill BMD software provides AI-automated assessment of bone mineral density from non-contrast abdominal computed tomography scans.

In a new study involving over 120 women, nearly two-thirds of whom had a family history of breast cancer, ultrafast MRI findings revealed a 5 percent increase in malignancy risk for each second increase in the difference between lesion and background parenchymal enhancement (BPE) time to enhancement (TTE).

Artificial intelligence (AI) software demonstrated a 92 percent sensitivity for detecting fractures in a study involving over 1,600 X-rays from a tertiary pediatric emergency department.

A systematic review of 147 clinical trials assessing neoadjuvant systemic therapy for breast cancer also revealed that mid-treatment imaging was utilized in 56 percent of the studies.

Going into effect in 2026, the new CPT codes may facilitate increased adoption of the CaRi-Heart software for detecting coronary inflammation from computed tomography scans pending FDA clearance of the technology.

While standalone AI interpretation offered over 10 percent higher sensitivity than radiologists for patients undergoing post-mastectomy surveillance mammography, it missed over 30 percent of breast cancers.