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.”)
Newsletter
Stay at the forefront of radiology with the Diagnostic Imaging newsletter, delivering the latest news, clinical insights, and imaging advancements for today’s radiologists.
Stroke MRI Study Assesses Impact of Motion Artifacts Upon AI and Radiologist Lesion Detection
July 16th 2025Noting a 7.4 percent incidence of motion artifacts on brain MRI scans for suspected stroke patients, the authors of a new study found that motion artifacts can reduce radiologist and AI accuracy for detecting hemorrhagic lesions.
