Researchers from the University of Minnesota have developed an artificial intelligence (AI) algorithm that evaluates chest x-rays for the diagnosis of COVID-19.
A team co-led by Dr. Christopher Tignanelli of the University of Minnesota has developed the algorithm in partnership with M Health Fairview and medical records software firm Epic. It will be made available for free through Epic, the group said.
The algorithm evaluates chest x-rays acquired in the emergency department of patients with suspected COVID-19. Research co-lead Ju Sun, PhD, and colleagues used 100,000 x-rays of patients without COVID-19 and 18,000 of patients with the disease to validate the algorithm.
"This may help patients get treated sooner and prevent unintentional exposure to COVID-19 for staff and other patients in the emergency department," Tignanelli said in a statement released by the university. "This can supplement nasopharyngeal swabs and diagnostic testing, which currently face supply chain issues and slow turnaround times across the country."
![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)
