Philips Introduces AI-Enhanced CT System for High-Volume Radiology Screening Programs
The artificial intelligence (AI)-enabled CT 3500 system reportedly reduces patient positioning time by 23 percent, improves low-contrast detectability by 60 percent and facilitates up to an 80 percent reduction in radiation dosing.
Philips has launched the CT 3500, a new computed tomography (CT) system that offers a variety of artificial intelligence (AI)-powered features for bolstering the efficiency of radiology workflows and enhancing image quality.
Key features of the CT 3500 system for improving radiology workflows include Precise Position, which provides automated determinations of patient orientation. Philips said this feature reduces patient positioning time by up to 23 percent and increases positioning accuracy by 50 percent.
The CT 3500 system also offers AI-based reconstruction via the Precise Image feature, which facilitates up to an 85 percent in noise and an 80 percent reduction in radiation dosing while achieving up to a 60 percent improvement in low-contrast detectability.
“Today, many radiology departments scan hundreds of patients a day. We’ve engineered the Philips CT 3500 to reduce the pain points that these high-volume departments face by developing a versatile, reliable, high-throughput imaging solution. It automates radiographers’ most time-consuming steps so that they can spend more time focusing on the patient,” noted Frans Venker, the general manager of computed tomography at Philips.
(Editor’s note: For related content, see “Study Shows Significant Incidental Findings in Nearly 34 Percent of Lung Screening CT Exams” and “AI-Based Denoising Technique Achieves 76 Percent Reduction in Radiation Dosing for CT Lung Cancer Screening.”)
How to Successfully Launch a CCTA Program at Your Hospital or Practice
June 11th 2025Emphasizing increasing recognition of the capability of coronary computed tomography angiography (CCTA) for the evaluation of acute and stable chest pain, this author defuses common misperceptions and reviews key considerations for implementation of a CCTA program.
Study: AI-Generated ADC Maps from MRI More Than Double Specificity in Prostate Cancer Detection
June 5th 2025Emerging research showed that AI-generated ADC mapping from MRI led to significant increases in accuracy, PPV and specificity in comparison to conventional ADC mapping while achieving a 93 percent sensitivity for PCa.
