
Open-source software provider Red Hat and artificial intelligence (AI) firm DarwinAI have partnered to accelerate the adoption of the COVID-Net AI program in hospitals.
DarwinAI developed the COVID-19 program with the University of Waterloo to improve COVID-19 detection and risk stratification on chest radiography. In March, a Canadian team published research showing the COVID-Net deep convolutional neural network achieved promising early results, including 100% sensitivity, 80% positive predictive value, and 83% accuracy for identifying COVID-19 on a small test set of chest radiography studies.
Now, Red Hat and DarwinAI are gearing the tool up for clinical and research use with the help of underlying technology from a computational research group at the Boston Children's Hospital. The collaboration aims to make COVID-Net easier for clinicians to use, including through a web-based graphical user interface designed to work with Boston Children's open-source ChRIS framework.
Rudolph Pienaar, PhD, the lead ChRIS technical architect and assistant professor in radiology at Harvard Medical School, said COVID-Net will help screen many cases at a large scale to focus on applying healthcare resources where they are most needed.















![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&fit=crop&h=112&q=70&w=112)



