RadNet subsidiary DeepHealth plans to share results from a study that explores the identification of incidental lung nodules aided by AI software at the upcoming ECR meeting in Vienna.
The results from the study will be presented during a March 2 session called "Pulmonary Nodules and Lung Cancer Screening" (RPS 2104) by Maurits Engbersen, Clinical Manager, DeepHealth. The research investigated the effect of computer-aided detection on incidental lung nodule management recommendations from a reporting radiologist and an expert peer reviewer, the company said.
The firm also plans to host a panel on February 29 called "Radiologists and AI-powered Health Informatics: Leading the Way in Screening" that will include Marie Pierre Revel, MD, of the University of Paris; Sam Hare, MD, CEO of Heart and Lung Health; and Greg Sorensen, MD, RadNet's CSO and DeepHealth's chief product leader for clinical AI.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
