AI

The newly FDA-cleared PACS Viewer from CliniComp reportedly allows direct review and analysis of imaging within a patient’s chart.

In a recent interview, Nina Kottler, MD, offered her perspective on findings from a recent report on AI in health care, AI integration challenges and what the future holds with AI in radiology.

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

Emerging research demonstrates that those with total plaque volume > 750 mm3 have an 18.6 percent risk of MACE at four years in comparison to a 1.4 percent MACE risk for those with no plaque.

Catch up on the most-well viewed radiology content in June 2026.

Catch up on the most well-viewed video interviews from Diagnostic Imaging in June 2026.

In order to fully take advantage of the capabilities of AI, this author emphasizes key principles and a framework for converting isolated technology solutions into reliable components of radiology workflows and operations.

Catch up on a variety of FDA news in radiology from the past week.

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

The new functionalities for the AI-enabled Breast Suite software include automated identification of breast arterial calcifications and integration of prior mammograms.

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

The First Read software provides preliminary drafting of radiology reports based on AI assessment of chest X-rays.

The Oncology Virtual Expert AI software reportedly provides semi-automated segmentation maps of brain tumors based on standard brain MRI scans.

The newly launched HOPPR Presto Agent reporting platform can be integrated into existing radiology systems and AI models without the need for new software.

In a recent interview, Constance Lehman, MD, PhD, discussed newly published research demonstrating the dynamic capability of AI-based risk scores, drawn from screening mammograms, to facilitate risk-adaptive screening.

The VascularAssist Occlusion Triage software reportedly demonstrated over a 90 percent sensitivity for detecting peripheral artery disease (PAD) on CT in clinical performance testing.

For pancreatic cancers less than or equal to 20 mm, a deep learning model offered a nearly 45 percent higher sensitivity rate with non-contrast CT in comparison to unassisted radiologist interpretation.

While use of an AI software demonstrated significantly higher accuracy in evaluating BI-RADS 2 presentations and no significant difference with radiologists in BI-RADS 4 assessments of breast ultrasound in a sub-analysis, researchers noted the exclusion of galactoceles, fluid collections and skin lesions that are commonly found in pregnant and lactating women.

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

In a recent interview, Nina Kottler, MD, discussed the challenges with traditional radiology reporting, its impact on cognitive load and the recently launched AI-enabled platform Mosaic Reporting.

The AI-enabled image processing with SubtleHD(CT) software reportedly facilitates improved noise reduction and image quality for computed tomography (CT) exams.

The Reporting Pro software reportedly offers an array of AI-enabled features that may help streamline radiology reports.

In a new survey of over 2,000 health-care professionals on AI use, respondents cited benefits ranging from improved work-life balance to preventing medical errors as well as concerns about overreliance on AI and inconsistent or limited training.

David Gruen, MD, MBA, FACR, of Advanced Radiology Consultants, shares how leading programs turned clinical evidence into operational reality.

What's already being done with large language models in radiology practice today.